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 Webster, R.
 2nd ed.  Chichester ; Hoboken, NJ : Wiley, c2007.
 Description
 Book — xii, 315 p. : ill. ; 24 cm.
 Summary

 Preface
 1 Introduction
 2 Basic Statistics
 3 Prediction and Interpolation
 4 Characterizing Spatial Processes: The Covariance and Variogram
 5 Modelling the Variogram
 6 Reliability of the Experimental Variogram and Nested Sampling
 7 Spectral Analysis
 8 Local Estimation or Prediction: Kriging
 9 Kriging in the Presence of Trend and Factorial Kriging
 10 CrossCorrelation, Coregionalization and Cokriging
 11 Disjunctive Kriging
 12 Stochastic Simulation (new file) Appendix A Appendix B References Index.
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 Online

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 Google Books (Full view)
 Weinheim : WileyVCH ; Chichester : John Wiley [distributor], 2010.
 Description
 Book — 1 online resource (400 pages)
 Summary

 Preface (EmmertStreib and Dehmer) GENERAL BIOLOGICAL AND STATISTICAL BASICS The biology of MYC in health and disease: a high altitude view (Turner, Bird and Refaeli) Cancer Stem Cells  Finding and Hitting the Roots of Cancer (Buss and Ho) Multiple Testing Methods (Farcomeni) STATISTICAL AND COMPUTATIONAL ANALYSIS METHODS Making Mountains Out of Molehills: Moving from Single Gene to Pathway Based Models of Colon Cancer Progression (Edelman, Garman, Potti, Mukherjee) GeneSet Expression Analysis: Challenges and Tools (Oron) Hotelling's T2 multivariate profiling for detecting differential expression in microarrays (Lu, Liu, Deng) Interpreting differential coexpression of gene sets (Ju Han Kim, Sung Bum Cho, Jihun Kim) Multivariate analysis of microarray data: Application of MANOVA (Hwang and Park) Testing Significance of a Class of Genes (Chen and Tsai) Differential dependency network analysis to identify topological changes in biological networks (Zhang, Li, Clarke, HilakiviClarke and Wang) An Introduction to TimeVarying Connectivity Estimation for Gene Regulatory Networks (Fujita, Sato, Almeida Demasi, Miyano, Cleide Sogayar, and Ferreira) A systems biology approach to construct a cancerperturbed proteinprotein interaction network for apoptosis by means of microarray and database mining (Chu and Chen) NN, title not confirmed (Fishel, Ruppin) Kernel Classification Methods for Cancer Microarray Data (Kato and Fujibuchi) Predicting Cancer Survival Using Expression Patterns (Reddy, Kronek, Brannon, Seiler, Ganesan, Rathmell, Bhanot) Integration of microarray data sets (Kim and Rha) Model Averaging For Biological Networks With Prior Information (Mukherjeea, Speed and Hill).
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Hoboken, N.J. : Wiley, c2008.
 Description
 Book — xvii, 429 p. : ill. ; 25 cm.
 Summary

 Preface. Acknowledgements. Contributor List.
 Section I: Overview of ECommerce Research Challenges.
 1. Statistical Challenges in Internet Advertising (Deepak Agarwal).
 2. How Has ECommerce Research Advanced Understanding of the Offline World (Chris Forman and Avi Goldfarb)?
 3. The Economic Impact of UserGenerated and FirmGenerated Online Content: Directions for Advancing the Frontiers in Electronic Commerce Research (Anindya Ghose).
 4. Is Privacy Protection for Data in an ECommerce World an Oxymoron (Stephen E. Fienberg)?
 5. Network Analysis of Wikipedia (Robert H. Warren, Edoardo M. Airoldi, and David L. Banks).
 Section II: ECommerce Applications.
 6. An Analysis of Price Dynamics, Bidder Networks, and Market Structure in Online Art Auctions (Mayukh Dass and Srinivas K. Reddy).
 7. Modeling Web Usability Diagnostics on the Basis of Usage Statistics (Avi Harel, Ron S. Kenett, and Fabrizio Ruggeri).
 8. Developing Rich Insights on Public Internet Firm Entry and Exit Based on Survival Analysis and Data Visualization (Robert J. Kauffman and Bin Wang).
 9. Modeling TimeVarying Coefficients in Pooled CrossSectional ECommerce Data: An Introduction (Eric Overby and Benn Konsynski).
 10. Optimization of Search Engine Marketing Bidding Strategies Using Statistical Techniques (Alon Matas and Yoni Schamroth).
 Section III: New Methods For ECommerce Data.
 11. Clustering Data with Measurement Errors (Mahesh Kumar and Nitin R. Patel).
 12. Functional Data Analysis for Sparse Auction Data (Bitao Liu and HansGeorg Muller).
 13. A Family of Growth Models for Representing the Price Process in Online Auctions (Valerie Hyde, Galit Shmueli, and Wolfgang Jank).
 14. Models of Bidder Activity Consistent with SelfSimilar Bid Arrivals (Ralph P. Russo, Galit Shmueli, and Nariankadu D. Shyamalkumar).
 15. Dynamic Spatial Models for Online Markets (Wolfgang Jank and P.K. Kannan).
 16. Differential Equation Trees to Model Price Dynamics in Online Auctions (Wolfgang Jank, Galit Shmueli, and Shanshan Wang).
 17. Quantile Modeling for Wallet Estimation (Claudia Perlich and Saharon Rosset).
 18. Applications of Randomized Response Methodology in ECommerce (Peter G.M. van der Heijden and Ulf Bockenholt). Index.
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4. The mathematics of derivatives securities with applications in MATLAB [electronic resource] [2012]
 Cerrato, Mario.
 Hoboken : John Wiley & Sons Inc., 2012.
 Description
 Book — xii, 236 pages : illustrations ; 24 cm
 Summary

 Preface xi
 1 An Introduction to Probability Theory
 1 1.1 The Notion of a Set and a Sample Space
 1 1.2 Sigma Algebras or Field
 2 1.3 Probability Measure and Probability Space
 2 1.4 Measurable Mapping
 3 1.5 Cumulative Distribution Functions
 4 1.6 Convergence in Distribution
 5 1.7 Random Variables
 5 1.8 Discrete Random Variables
 6 1.9 Example of Discrete Random Variables: The Binomial Distribution
 6 1.10 Hypergeometric Distribution
 7 1.11 Poisson Distribution
 8 1.12 Continuous Random Variables
 9 1.13 Uniform Distribution
 9 1.14 The Normal Distribution
 9 1.15 Change of Variable
 11 1.16 Exponential Distribution
 12 1.17 Gamma Distribution
 12 1.18 Measurable Function
 13 1.19 Cumulative Distribution Function and Probability Density Function
 13 1.20 Joint, Conditional and Marginal Distributions
 17 1.21 Expected Values of Random Variables and Moments of a Distribution
 19
 2 Stochastic Processes
 25 2.1 Stochastic Processes
 25 2.2 Martingales Processes
 26 2.3 Brownian Motions
 29 2.4 Brownian Motion and the Reflection Principle
 32 2.5 Geometric Brownian Motions
 35
 3 Ito Calculus and Ito Integral
 37 3.1 Total Variation and Quadratic Variation of Differentiable Functions
 37 3.2 Quadratic Variation of Brownian Motions
 39 3.3 The Construction of the Ito Integral
 40 3.4 Properties of the Ito Integral
 41 3.5 The General Ito Stochastic Integral
 42 3.6 Properties of the General Ito Integral
 43 3.7 Construction of the Ito Integral with Respect to SemiMartingale Integrators
 44 3.8 Quadratic Variation of a General Bounded Martingale
 46
 4 The Black and Scholes Economy
 55 4.1 Introduction
 55 4.2 Trading Strategies and Martingale Processes
 55 4.3 The Fundamental Theorem of Asset Pricing
 56 4.4 Martingale Measures
 58 4.5 Girsanov Theorem
 59 4.6 RiskNeutral Measures
 62
 5 The Black and Scholes Model
 67 5.1 Introduction
 67 5.2 The Black and Scholes Model
 67 5.3 The Black and Scholes Formula
 68 5.4 Black and Scholes in Practice
 70 5.5 The FeynmanKac Formula
 71
 6 Monte Carlo Methods
 79 6.1 Introduction
 79 6.2 The Data Generating Process (DGP) and the Model
 79 6.3 Pricing European Options
 80 6.4 Variance Reduction Techniques
 81
 7 Monte Carlo Methods and American Options
 91 7.1 Introduction
 91 7.2 Pricing American Options
 91 7.3 Dynamic Programming Approach and American Option Pricing
 92 7.4 The Longstaff and Schwartz Least Squares Method
 93 7.5 The Glasserman and Yu Regression Later Method
 95 7.6 Upper and Lower Bounds and American Options
 96
 8 American Option Pricing: The Dual Approach
 101 8.1 Introduction
 101 8.2 A General Framework for American Option Pricing
 101 8.3 A Simple Approach to Designing Optimal Martingales
 104 8.4 Optimal Martingales and American Option Pricing
 104 8.5 A Simple Algorithm for American Option Pricing
 105 8.6 Empirical Results
 106 8.7 Computing Upper Bounds
 107 8.8 Empirical Results
 109
 9 Estimation of Greeks using Monte Carlo Methods
 113 9.1 Finite Difference Approximations
 113 9.2 Pathwise Derivatives Estimation
 114 9.3 Likelihood Ratio Method
 116 9.4 Discussion
 118
 10 Exotic Options
 121 10.1 Introduction
 121 10.2 Digital Options
 121 10.3 Asian Options
 122 10.4 Forward Start Options
 123 10.5 Barrier Options
 123 10.5.1 Hedging Barrier Options
 125
 11 Pricing and Hedging Exotic Options
 129 11.1 Introduction
 129 11.2 Monte Carlo Simulations and Asian Options
 129 11.3 Simulation of Greeks for Exotic Options
 130 11.4 Monte Carlo Simulations and Forward Start Options
 131 11.5 Simulation of the Greeks for Exotic Options
 132 11.6 Monte Carlo Simulations and Barrier Options
 132
 12 Stochastic Volatility Models
 137 12.1 Introduction
 137 12.2 The Model
 137 12.3 Square Root Diffusion Process
 138 12.4 The Heston Stochastic Volatility Model (HSVM)
 139 12.5 Processes with Jumps
 143 12.6 Application of the Euler Method to Solve SDEs
 143 12.7 Exact Simulation Under SV
 144 12.8 Exact Simulation of Greeks Under SV
 146
 13 Implied Volatility Models
 151 13.1 Introduction
 151 13.2 Modelling Implied Volatility
 152 13.3 Examples
 153
 14 Local Volatility Models
 157 14.1 An Overview
 157 14.2 The Model
 159 14.3 Numerical Methods
 161
 15 An Introduction to Interest Rate Modelling
 167 15.1 A General Framework
 167 15.2 Affine Models (AMs)
 169 15.3 The Vasicek Model
 171 15.4 The Cox, Ingersoll and Ross (CIR) Model
 173 15.5 The Hull and White (HW) Model
 174 15.6 The Black Formula and Bond Options
 175
 16 Interest Rate Modelling
 177 16.1 Some Preliminary Definitions
 177 16.2 Interest Rate Caplets and Floorlets
 178 16.3 Forward Rates and Numeraire
 180 16.4 Libor Futures Contracts
 181 16.5 Martingale Measure
 183
 17 Binomial and Finite Difference Methods
 185 17.1 The Binomial Model
 185 17.2 Expected Value and Variance in the Black and Scholes and Binomial Models
 186 17.3 The CoxRossRubinstein Model
 187 17.4 Finite Difference Methods
 188
 Appendix 1 An Introduction to MATLAB
 191 A1.1 What is MATLAB?
 191 A1.2 Starting MATLAB
 191 A1.3 Main Operations in MATLAB
 192 A1.4 Vectors and Matrices
 192 A1.5 Basic Matrix Operations
 194 A1.6 Linear Algebra
 195 A1.7 Basics of Polynomial Evaluations
 196 A1.8 Graphing in MATLAB
 196 A1.9 Several Graphs on One Plot
 197 A1.10 Programming in MATLAB: Basic Loops
 199 A1.11 MFile Functions
 200 A1.12 MATLAB Applications in Risk Management
 200 A1.13 MATLAB Programming: Application in Financial Economics
 202
 Appendix 2 Mortgage Backed Securities
 205 A2.1 Introduction
 205 A2.2 The Mortgage Industry
 206 A2.3 The Mortgage Backed Security (MBS) Model
 207 A2.4 The Term Structure Model
 208 A2.5 Preliminary Numerical Example
 210 A2.6 Dynamic Option Adjusted Spread
 210 A2.7 Numerical Example
 212 A2.8 Practical Numerical Examples
 213 A2.9 Empirical Results
 214 A2.10 The PrePayment Model
 215
 Appendix 3 Value at Risk
 217 A3.1 Introduction
 217 A3.2 Value at Risk (VaR)
 217 A3.3 The Main Parameters of a VaR
 218 A3.4 VaR Methodology
 219 A3.5 Empirical Applications
 222 A3.6 Fat Tails and VaR
 224 Bibliography
 227 References
 229 Index 233.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
The book is divided into two parts  the first part introduces probability theory, stochastic calculus and stochastic processes before moving on to the second part which instructs readers on how to apply the content learnt in part one to solve complex financial problems such as pricing and hedging exotic options, pricing American derivatives, pricing and hedging under stochastic volatility, and interest rate modelling. Each chapter provides a thorough discussion of the topics covered with practical examples in MATLAB so that readers will build up to an analysis of modern cutting edge research in finance, combining probabilistic models and cutting edge finance illustrated by MATLAB applications.Most books currently available on the subject require the reader to have some knowledge of the subject area and rarely consider computational applications such as MATLAB. This book stands apart from the rest as it covers complex analytical issues and complex financial instruments in a way that is accessible to those without a background in probability theory and finance, as well as providing detailed mathematical explanations with MATLAB code for a variety of topics and real world case examples.
(source: Nielsen Book Data)
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 Google Books (Full view)
5. Probability and statistics for finance [2010]
 Hoboken, N.J. : John Wiley & Sons, ©2010.
 Description
 Book — 1 online resource (xviii, 654 pages) : illustrations.
 Summary

 Preface xv About the Authors xvii
 CHAPTER 1 Introduction
 1 Probability vs. Statistics
 4 Overview of the Book
 5 Part One Descriptive Statistics
 15
 Chapter 2 Basic Data Analysis
 17 Data Types
 17 Frequency Distributions
 22 Empirical Cumulative Frequency Distribution
 27 Data Classes
 32 Cumulative Frequency Distributions
 41 Concepts Explained in this
 Chapter 43
 Chapter 3 Measures of Location and Spread
 45 Parameters vs. Statistics
 45 Center and Location
 46 Variation
 59 Measures of the Linear Transformation
 69 Summary of Measures
 71 Concepts Explained in this
 Chapter 73
 Chapter 4 Graphical Representation of Data
 75 Pie Charts
 75 Bar Chart
 78 Stem and Leaf Diagram
 81 Frequency Histogram
 82 Ogive Diagrams
 89 Box Plot
 91 QQ Plot
 96 Concepts Explained in this
 Chapter 99
 CHAPTER 5 Multivariate Variables and Distributions
 101 Data Tables and Frequencies
 101 Class Data and Histograms
 106 Marginal Distributions
 107 Graphical Representation
 110 Conditional Distribution
 113 Conditional Parameters and Statistics
 114 Independence
 117 Covariance
 120 Correlation
 123 Contingency Coefficient
 124 Concepts Explained in this
 Chapter 126
 CHAPTER 6 Introduction to Regression Analysis
 129 The Role of Correlation
 129 Regression Model: Linear Functional Relationship Between Two Variables
 131 Distributional Assumptions of the Regression Model
 133 Estimating the Regression Model
 134 Goodness of Fit of the Model
 138 Linear Regression of Some Nonlinear Relationship
 140 Two Applications in Finance
 142 Concepts Explained in this
 Chapter 149
 CHAPTER 7 Introduction to Time Series Analysis
 153 What Is Time Series?
 153 Decomposition of Time Series
 154 Representation of Time Series with Difference Equations
 159 Application: The Price Process
 159 Concepts Explained in this
 Chapter 163 Part Two Basic Probability Theory
 165
 CHAPTER 8 Concepts of Probability Theory
 167 Historical Development of Alternative Approaches to Probability
 167 Set Operations and Preliminaries
 170 Probability Measure
 177 Random Variable
 179 Concepts Explained in this
 Chapter 185
 Chapter 9 Discrete Probability Distributions
 187 Discrete Law
 187 Bernoulli Distribution
 192 Binomial Distribution
 195 Hypergeometric Distribution
 204 Multinomial Distribution
 211 Poisson Distribution
 216 Discrete Uniform Distribution
 219 Concepts Explained in this
 Chapter 221
 CHAPTER 10 Continuous Probability Distributions
 229 Continuous Probability Distribution Described
 229 Distribution Function
 230 Density Function
 232 Continuous Random Variable
 237 Computing Probabilities from the Density Function
 238 Location Parameters
 239 Dispersion Parameters
 239 Concepts Explained in this
 Chapter 245
 CHAPTER 11 Continuous Probability Distributions with Appealing Statistical Properties
 247 Normal Distribution
 247 ChiSquare Distribution
 254 Student s tDistribution
 256 FDistribution
 260 Exponential Distribution
 262 Rectangular Distribution
 266 Gamma Distribution
 268 Beta Distribution
 269 LogNormal Distribution
 271 Concepts Explained in this
 Chapter 275
 CHAPTER 12 Continuous Probability Distributions Dealing with Extreme Events
 277 Generalized Extreme Value Distribution
 277 Generalized Pareto Distribution
 281 Normal Inverse Gaussian Distribution
 283 Stable Distribution
 285 Concepts Explained in this
 Chapter 292
 CHAPTER 13 Parameters of Location and Scale of Random Variables
 295 Parameters of Location
 296 Parameters of Scale
 306 Concepts Explained in this
 Chapter 321 Appendix: Parameters for Various Distribution Functions
 322
 Chapter 14 Joint Probability Distributions
 325 Higher Dimensional Random Variables
 326 Joint Probability Distribution
 328 Marginal Distributions
 333 Dependence
 338 Covariance and Correlation
 341 Selection of Multivariate Distributions
 347 Concepts Explained in this
 Chapter 358
 Chapter 15 Conditional Probability and Bayes Rule
 361 Conditional Probability
 362 Independent Events
 365 Multiplicative Rule of Probability
 367 Bayes Rule
 372 Conditional Parameters
 374 Concepts Explained in this
 Chapter 377
 CHAPTER 16 Copula and Dependence Measures
 379 Copula
 380 Alternative Dependence Measures
 406 Concepts Explained in this
 Chapter 412 Part Three Inductive Statistics
 413
 Chapter 17 Point Estimators
 415 Sample, Statistic, and Estimator
 415 Quality Criteria of Estimators
 428 Large Sample Criteria
 435 Maximum Likehood Estimator
 446 Exponential Family and Sufficiency
 457 Concepts Explained in this
 Chapter 461
 Chapter 18 Confidence Intervals
 463 Confidence Level and Confidence Interval
 463 Confidence Interval for the Mean of a Normal Random Variable
 466 Confidence Interval for the Mean of a Normal Random Variable with Unknown Variance
 469 Confidence Interval for the Variance of a Normal Random Variable
 471 Confidence Interval for the Variance of a Normal Random Variable with Unknown Mean
 474 Confidence Interval for the Parameter p of a Binomial Distribution
 475 Confidence Interval for the Parameter of an Exponential Distribution
 477 Concepts Explained in this
 Chapter 479
 Chapter 19 Hypothesis Testing
 481 Hypotheses
 482 Error Types
 485 Quality Criteria of a Test
 490 Examples
 496 Concepts Explained in this
 Chapter 518 Part Four Multivariate Linear Regression Analysis
 519
 CHAPTER 20 Estimates and Diagnostics for Multivariate Linear Regression Analysis
 521 The Multivariate Linear Regression Model
 522 Assumptions of the Multivariate Linear Regression Model
 523 Estimation of the Model Parameters
 523 Designing the Model
 526 Diagnostic Check and Model Significance
 526 Applications to Finance
 531 Concepts Explained in this
 Chapter 543
 CHAPTER 21 Designing and Building a Multivariate Linear Regression Model
 545 The Problem of Multicollinearity
 545 Incorporating Dummy Variables as Independent Variables
 548 Model Building Techniques
 561 Concepts Explained in this
 Chapter 565
 CHAPTER 22 Testing the Assumptions of the Multivariate Linear Regression Model
 567 Tests for Linearity
 568 Assumed Statistical Properties about the Error Term
 570 Tests for the Residuals Being Normally Distributed
 570 Tests for Constant Variance of the Error Term (Homoskedasticity)
 573 Absence of Autocorrelation of the Residuals
 576 Concepts Explained in this
 Chapter 581 Appendix A Important Functions and Their Features
 583 Continuous Function
 583 Indicator Function
 586 Derivatives
 587 Monotonic Function
 591 Integral
 592 Some Functions
 596 Appendix B Fundamentals of Matrix Operations and Concepts
 601 The Notion of Vector and Matrix
 601 Matrix Multiplication
 602 Particular Matrices
 603 Positive Semidefinite Matrices
 614 APPENDIX C Binomial and Multinomial Coefficients
 615 Binomial Coefficient
 615 Multinomial Coefficient
 622 APPENDIX D Application of the LogNormal Distribution to the Pricing of Call Options
 625 Call Options
 625 Deriving the Price of a European Call Option
 626 Illustration
 631 References
 633 Index 635.
 (source: Nielsen Book Data)
 Preface. About the Authors.
 CHAPTER 1 Introduction. Probability Versus Statistics. Overview of the Book. PART ONE Descriptive Statistics.
 CHAPTER 2 Basic Data Analysis. Data Types. Frequency Distributions. Empirical Cumulative Frequency Distribution. Data Classes. Cumulative Frequency Distributions. Concepts Explained in this Chapter (In Order of Presentation).
 CHAPTER 3 Measures of Location and Spread. Parameters versus Statistics. Center and Location. Variation. Measures of the Linear Transformation. Summary of Measures. Concepts Explained in this Chapter (In Order of Presentation).
 CHAPTER 4 Graphical Representation of Data. Pie Charts. Bar Chart. Stem and Leaf Diagram. Frequency Histogram. Ogive Diagrams. Box Plot. QQ Plot. Concepts Explained in this Chapter (In Order of Presentation).
 CHAPTER 5 Multivariate Variables and Distributions. Data Tables and Frequencies. Class Data and Histograms. Marginal Distributions. Graphical Representation. Conditional Distribution. Conditional Parameters and Statistics. Independence. Covariance. Correlation. Contingency Coefficient. Concepts Explained in this Chapter (In Order of Presentation).
 CHAPTER 6 Introduction to Regression Analysis. The Role of Correlation. Regression Model: Linear Functional Relationship Between Two Variables. Distributional Assumptions of the Regression Model. Estimating the Regression Model. Goodness of Fit of the Model. Linear Regression of Some NonLinear Relationship. Two Applications in Finance. Concepts Explained in this Chapter (In Order of Presentation).
 CHAPTER 7 Introduction to Time Series Analysis. What Is Time Series? Decomposition of Time Series. Representation of Time Series with Difference Equations. Application: The Price Process. Concepts Explained in this Chapter (In Order of Presentation). PART TWO Basic Probability Theory.
 CHAPTER 8 Concepts of Probability Theory. Historical Development of Alternative Approaches to Probability. Set Operations and Preliminaries. Probability Measure. Random Variable. Concepts Explained in this Chapter (In Order of Presentation).
 CHAPTER 9 Discrete Probability Distributions. Discrete Law. Bernoulli Distribution. Binomial Distribution. Hypergeometric Distribution. Multinomial Distribution. Poisson Distribution Discrete Uniform Distribution. Concepts Explained in this Chapter (In Order of Presentation).
 CHAPTER 10 Continuous Probability Distributions. Continuous Probability Distribution Described. Distribution Function. Density Function. Continuous Random Variable. Computing Probabilities from the Density Function. Location Parameters. Dispersion Parameters. Concepts Explained in this Chapter (In Order of Presentation).
 CHAPTER 11 Continuous Probability Distributions with Appealing Statistical Properties. Normal Distribution. ChiSquare Distribution. Student's t Distribution. F Distribution. Exponential Distribution. Rectangular Distribution. Gamma Distribution. Beta Distribution. LogNormal Distribution. Concepts Explained in this Chapter (In Order of Presentation).
 CHAPTER 12 Continuous Probability Distributions Dealing with Extreme Events. Generalized Extreme Value Distribution. Generalized Pareto Distribution. Normal Inverse Gaussian Distribution. aStable Distribution. Concepts Explained in this Chapter (In Order of Presentation).
 CHAPTER 13 Parameters of Location and Scale of Random Variables. Parameters of Location. Parameters of Scale. Concepts Explained in this Chapter (In Order of Presentation). Appendix: Parameters for Various Distribution Functions.
 CHAPTER 14 Joint Probability Distributions. Higher Dimensional Random Variables. Joint Probability Distribution. Marginal Distributions. Dependence. Covariance and Correlation. Selection of Multivariate Distributions. Concepts Explained in this Chapter (In Order of Presentation).
 CHAPTER 15 Conditional Probability and Bayes' Rule. Conditional Probability. Independent Events. Multiplicative Rule of Probability. Bayes' Rule. Conditional Parameters. Concepts Explained in this Chapter (In Order of Presentation).
 CHAPTER 16 Copula and Dependence Measures. Copula. Alternative Dependence Measures. Concepts Explained in this Chapter (In Order of Presentation). PART THREE Inductive Statistics.
 CHAPTER 17 Point Estimators. Sample, Statistic, and Estimator. Quality Criteria of Estimators. Large Sample Criteria. Maximum Likehood Estimator. Exponential Family and Sufficiency. Concepts Explained in this Chapter (In Order of Presentation).
 CHAPTER 18 Confidence Intervals. Confidence Level and Confidence Interval. Confidence Interval for the Mean of a Normal Random Variable. Confidence Interval for the Mean of a Normal Random Variable with Unknown Variance. Confidence Interval for the Parameter p of a Binomial Distribution. Concepts Explained in this Chapter (In Order of Presentation).
 CHAPTER 19 Hypothesis Testing. Hypotheses. Error Types. Quality Criteria of a Test. Examples. Concepts Explained in this Chapter (In Order of Presentation). PART FOUR Multivariate Linear Regression Analysis.
 CHAPTER 20 Estimates and Diagnostics for Multivariate Linear Regression Analysis. The Multivariate Linear Regression Model. Assumptions of the Multivariate Linear Regression Model. Estimation of the Model Parameters. Designing the Model. Diagnostic Check and Model Significance. Applications to Finance. Concepts Explained in this Chapter (In Order of Presentation).
 CHAPTER 21 Designing and Building a Multivariate Linear Regression Model. The Problem of Multicollinearity. Incorporating Dummy Variables as Independent Variables. Model Building Techniques
 561 Concepts Explained in this Chapter (In Order of Presentation).
 CHAPTER 22 Testing the Assumptions of the Multivariate Linear Regression Model. Tests for Linearity. Assumed Statistical Properties About the Error Term. Tests for the Residuals Being Normally Distributed. Tests for Constant Variance of the Error Term (Homoskedasticity). Absence of Autocorrelation of the Residuals. Concepts Explained in this Chapter (In Order of Presentation). APPENDIX A Important Functions and Their Features. Continuous Function. Indicator Function. Derivatives. Monotonic Function. Integral. Some Functions. APPENDIX B Fundamentals of Matrix Operations and Concepts. The Notion of Vector and Matrix. Matrix Multiplication. Particular Matrices. Positive Semidefinite Matrices. APPENDIX C Binomial and Multinomial Coefficients. Binomial Coefficient. Multinomial Coefficient. APPENDIX D Application of the LogNormal Distribution to the Pricing of Call Options. Call Options. Deriving the Price of a European Call Option. Illustration. REFERENCES. INDEX.
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 Peat, Jennifer K.
 Chichester, West Sussex, UK ; Hoboken, NJ : WileyBlackwell/BMJ Books, 2008.
 Description
 Book — viii, 182 p. : ill. ; 28 cm.
 Summary

 Contents . Foreword . By Virginia A. Moyer . Introduction. Overview . UNIT
 1 Hypothesis testing and estimation. UNIT
 2 Incidence and prevalence rates . UNIT
 3 Comparing proportions . UNIT
 4 Relative risk and odds ratio . UNIT
 5 Clinical trials . UNIT
 6 Comparing mean values . UNIT
 7 Correlation and regression . UNIT
 8 Followup studies . UNIT
 9 Survival analyses . UNIT
 10 Diagnostic and screening statistics. Answers. Glossary. Index.
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 Zaĭchik, L. I. (Leonid Isaakovich)
 Weinheim : WileyVCH, c2008.
 Description
 Book — 1 online resource (xix, 297 p.) : ill.
 Summary

 Preface Introduction
 1 Motion of Particles and Heat Exchange in Homogeneous Isotropic Turbulence
 2 Motion of Particles in Gradient Turbulent Flows
 3 Heat Exchange of Particles in Gradient Turbulent Flows
 4 Collisions of Particles in a Turbulent Flow
 5 Relative Dispersion and Clustering of Monodispersed Particles in Homogeneous Turbulence
 6 Collision and Clustering of Bidispersed Particles in Homogeneous Turbulence.
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 Gibbons, Robert D., 1955
 2nd ed. / Dulal Bhaumik, Subhash Aryal.  Hoboken, N.J. : Wiley, c2009.
 Description
 Book — 1 online resource.
 Summary

 Preface. Acknowledgments. Acronyms.
 1 NORMAL PREDICTION INTERVALS. 1.1 Overview. 1.2 Prediction Intervals for the Next Single Measurement from a Normal Distribution. 1.3 Prediction Limits for the Next k Measurements from a Normal Distribution. 1.4 Normal Prediction Limits with Resampling. 1.5 Simultaneous Normal Prediction Limits for the Next k Samples. 1.6 Simultaneous Normal Prediction Limits for the Next r of m Measurements at Each of k Monitoring Wells. 1.7 Normal Prediction Limits for the Mean(s) of m >
 1 Future Measurements at Each of k Monitoring Wells. 1.8 Summary.
 2 NONPARAMETRIC PREDICTION INTERVALS. 2.1 Overview. 2.2 Pass
 1 of m Samples. 2.3 Pass m 
 1 of m Samples. 2.4 Pass First or all m 
 1 Resamples. 2.5 Nonparametric Prediction Limits for the Median of m Future Measurements at each of k Locations. 2.6 Summary.
 3 PREDICTION INTERVALS FOR OTHER DISTRIBUTIONS. 3.1 Overview. 3.2 Lognormal Distribution. 3.3 Lognormal Prediction Limits for the Median of m Future Measurements. 3.4 Lognormal Prediction Limits for the Mean of m Future Measurements. 3.5 Poisson Distribution. 3.6 Summary.
 4 GAMMA PREDICTION INTERVALS AND SOME RELATED TOPICS. 4.1 Overview. 4.2 Gamma Distribution. 4.3 Comparison of Gamma mean to a Regulatory Standard. 4.4 Summary.
 5 TOLERANCE INTERVALS. 5.1 Overview. 5.2 Normal Tolerance Limits. 5.3 Poisson Tolerance Limits. 5.4 Gamma Tolerance Limits. 5.5 Nonparametric Tolerance Limits. 5.6 Summary.
 6 METHOD DETECTION LIMITS. 6.1 Overview. 6.2 Single Concentration Designs. 6.3 Calibration Designs. 6.4 Summary.
 7 PRACTICAL QUANTITATION LIMITS. 7.1 Overview. 7.2 Operational Definition. 7.3 A Statistical Estimate of the PQL. 7.4 Derivation of the PQL. 7.5 A Simpler Alternative. 7.6 Uncertainty in Y alpha * . 7.7 The Effect of the Transformation. 7.8 Selecting N . 7.9 Summary.
 8 INTERLABORATORY CALIBRATION. 8.1 Overview. 8.2 General Random Effects Regression Model for the Case of Heteroscedastic Measurement Errors. 8.3 Estimation of Model Parameters. 8.4 Applications of the Derived Results. 8.5 Summary.
 9 CONTAMINANT SOURCE ANALYSIS. 9.1 Overview. 9.2 Statistical Classification Problems. 9.3 Nonparametric Methods. 9.4 Summary.
 10 INTRAWELL COMPARISON. 10.1 Overview. 10.2 Shewart Control Charts. 10.3 (CUSUM) Control Charts. 10.4 Combined ShewartCUSUM Control Charts. 10.5 Prediction Limits. 10.6 Pooling Variance Estimates. 10.7 Summary.
 11 TREND ANALYSIS. 11.1 Overview. 11.2 Sen Test. 11.3 MannKendall Test. 11.4 Seasonal Kendall Test. 11.5 Some Statistical Properties. 11.6 Summary.
 12 CENSORED DATA. 12.1 Conceptual Foundation. 12.2 Simple Substitution Methods. 12.3 Maximum Likelihood Estimators. 12.4 Restricted Maximum Likelihood Estimators. 12.5 Linear Estimators. 12.6 Alternative Linear Estimators. 12.7 Delta Distributions. 12.8 Regression Methods. 12.9 Substitution of Expected Values of Order Statistics. 12.10 Comparison of Estimators. 12.11 Some Simulation Results. 12.12 Summary.
 13 NORMAL PREDICTION LIMITS FOR LEFTCENSORED DATA. 13.1 Prediction Limit for LeftCensored Normal Data. 13.2 Simulation Study. 13.3 Summary.
 14 TESTS FOR DEPARTURE FROM NORMALITY. 14.1 Overview. 14.2 A Simple Graphical Approach. 14.3 The ShapiroWilk Test. 14.4 ShapiroFrancia Test. 14.5 D'Agostino Test. 14.6 Methods Based on Moments of a Normal Distribution. 14.7 Multiple Independent Samples. 14.8 Testing Normality in Censored Samples. 14.9 The KolmogorovSmirnov Test. 14.10 Summary.
 15 VARIANCE COMPONENT MODELS. 15.1 Overview. 15.2 LeastSquares Estimators. 15.3 Maximum Likelihood Estimators. 15.4 Summary.
 16 DETECTING OUTLIERS. 16.1 Overview. 16.2 Rosner Test. 16.3 Skewness Test. 16.4 Kurtosis Test. 16.5 ShapiroWilk Test. 16.6 E m statistic. 16.7 Dixon Test. 16.8 Summary.
 17 SURFACE WATER ANALYSIS. 17.1 Overview. 17.2 Statistical Considerations. 17.3 Statistical Power. 17.4 Summary.
 18 ASSESSMENT AND CORRECTIVE ACTION MONITORING. 18.1 Overview. 18.2 Strategy. 18.3 LCL or UCL? 18.4 Normal Confidence Limits for the Mean. 18.5 Lognormal Confidence Limits for the Median. 18.6 Lognormal Confidence Limits for the Mean. 18.7 Nonparametric Confidence Limits for the Median. 18.8 Confidence Limits for Other Percentiles of the Distribution. 18.9 Summary.
 19 REGULATORY ISSUES. 19.1 Regulatory Statistics. 19.2 Methods to be Avoided. 19.3 Verification Resampling. 19.4 Interwell versus Intrawell Comparisons. 19.5 Computer Software. 19.6 More Recent Developments.
 20 SUMMARY. Topic Index.
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9. Statistical framework for recreational water quality criteria and monitoring [electronic resource] [2007]
 Chichester ; Hoboken, NJ : John Wiley & Sons, c2007.
 Description
 Book — xi, 234 p. : ill. ; 24 cm.
 Summary

 Contributors. Preface.
 1: The Evolution of Water Quality Criteria in the United States  19222003 (Alfred P. Dufour and Stephen Schaub).
 2: A Management Context For The Statistical Design Of Recreational Contact Water Quality Monitoring Programs (Stephen B. Weisberg).
 3: Conceptual Bases for Relating Illness Risk to Indicator Concentrations (David F. Parkhurst, Guntehr F. Craun, and Jeffrey A. Soller).
 4: On Selecting the Statistical Rationale for Revised EPA Recreational Water Quality Criteria for Bacteria (Richard O. Gilbert).
 5: Sampling Recreational Waters (Abdel H ElShaarawi and Sylvia R Esterby).
 6: The Lognormal Distribution and Use of the Geometric Mean and the Arithmetic Mean in Recreational Water Quality Measurement (Larry J. Wymer and Timothy J. Wade).
 7: The EMPACT Beaches: A Case Study in Recreational Water Sampling (Larry J. Wymer).
 8: Microbial Risk Assessment Modeling (Graham McBride).
 9: A plausible model to explain concentrationresponse relationships in randomized controlled trials assessing infectious disease risks from exposure to recreational waters (Albrecht Wiedenmann).
 10: Nowcasting recreational water quality (Alexandria B. Boehm, Richard L. Whitman, Meredith B. Nevers, Deyi Hou, and Stephen B. Weisberg).
 11: Statistical sensitivity analysis and water quality (Alessandro Fasso). Index.
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 Bethlehem, Jelke G.
 Hoboken, N.J. : Wiley, c2009.
 Description
 Book — xi, 375 p. : ill., maps ; 25 cm.
 Summary

 Preface.
 1. The Survey Process. 1.1. About Surveys. 1.2. A Survey, StepbyStep. 1.3. Some History of Survey Research. 1.4. This Book. 1.5. Samplonia. Exercises.
 2. Basic Concepts. 2.1. The Survey Objectives. 2.2. The Target Population. 2.3. The Sampling Frame. 2.4. Sampling. 2.5. Estimation. Exercises.
 3. Questionnaire Design. 3.1. The Questionnaire. 3.2. Factual and Nonfactual Questions. 3.3. The Question Text. 3.4. Answer Types. 3.5. Question Order. 3.6. Questionnaire Testing. Exercises.
 4. Single Sampling Designs. 4.1. Simple Random Sampling. 4.2. Systematic Sampling. 4.3. Unequal Probability Sampling. 4.4. Systematic Sampling with Unequal Probabilities. Exercises.
 5. Composite Sampling Designs. 5.1. Stratified Sampling. 5.2. Cluster Sampling. 5.3. TwoStage Sampling. 5.4. TwoDimensional Sampling. Exercises.
 6. Estimators. 6.1. Use of Auxiliary Information. 6.2. A Descriptive Model. 6.3. The Direct Estimator. 6.4. The Ratio Estimator. 6.5. The Regression Estimator. 6.6. The Poststratification Estimator. Exercises.
 7. Data Collection. 7.1. Traditional Data Collection. 7.2. ComputerAssisted Interviewing. 7.3. MixedMode Data Collection. 7.4. Electronic Questionnaires. 7.5. Data Collection with Blaise. Exercises.
 8. The Quality of the Results. 8.1. Errors in Surveys. 8.2. Detection and Correction of Errors. 8.3. Imputation Techniques. 8.4. Data Editing Strategies. Exercises.
 9. The Nonresponse Problem. 9.1. Nonresponse. 9.2. Response Rates. 9.3. Models for Nonresponse. 9.4. Analysis of Nonresponse. 9.5. Nonresponse Correction Techniques. Exercises.
 10. Weighting Adjustment. 10.1. Introduction. 10.2. Poststratification. 10.3. Linear Weighting. 10.4. Multiplicative Weighting. 10.5. Calibration Estimation. 10.6. Other Weighting Issues. 10.7. Use of Propensity Scores. 10.8. A Practical Example. Exercises.
 11. Online Surveys. 11.1. The Popularity of Online Research. 11.2. Errors in Online Surveys. 11.3. The Theoretical Framework. 11.4. Correction by Adjustment Weighting. 11.5. Correction Using a Reference Survey. 11.6. Sampling the NonInternet Population. 11.7. Propensity Weighting. 11.8. Simulating the Effects of Undercoverage. 11.9. Simulating the Effects of SelfSelection. 11.10. About the Use of Online Surveys. Exercises.
 12. Analysis and Publication. 12.1. About Data Analysis. 12.2. The Analysis of Dirty Data. 12.3. Preparing a Survey Report. 12.4. Use of Graphs. Exercises.
 13. Statistical Disclosure Control. 13.1. Introduction. 13.2. The Basic Disclosure Problem. 13.3. The Concept of Uniqueness. 13.4. Disclosure Scenarios. 13.5. Models for the Disclosure Risk. 13.6. Practical Disclosure Protection. Exercises. References. Index.
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 Lachin, John M., 1942
 2nd ed.  Hoboken, N.J. : Wiley, ©2011.
 Description
 Book — 1 online resource (xxiii, 622 pages) : illustrations.
 Summary

 Preface. Preface to First Edition.
 1 Biostatistics and Biomedical Science. 1.1 Statistics and the Scientific Method. 1.2 Biostatistics. 1.3 Natural History of Disease Progression. 1.4 Types of Biomedical Studies. 1.5 Studies of Diabetic Nephropathy.
 2 Relative Risk Estimates and Tests for Independent Groups. 2.1 Probability As a Measure of Risk. 2.2 Measures of Relative Risk. 2.3 Large Sample Distribution. 2.4 Sampling Models Likelihoods. 2.5 Exact Inference. 2.6 Large Sample Inferences. 2.7 SAS PROC FREQ. 2.8 Other Measures of Differential Risk. 2.9 Polychotomous and Ordinal Data. 2.10 Two Independent Groups With Polychotomous Response. 2.11 Multiple Independent Groups. 2.12 Problems.
 3 Sample Size, Power, and Efficiency. 3.1 Estimation Precision. 3.2 Power of ZTests. 3.3 Test for Two Proportions. 3.4 Power of ChiSquare Tests. 3.5 SAS PROC POWER. 3.6 Efficiency. 3.7 Problems.
 4 StratifiedAdjusted Analysis for Independent Groups. 4.1 Introduction. 4.2 MantelHaenszel Test and Cochran's Test. 4.3 StratifiedAdjusted Estimators. 4.4 Nature of Covariate Adjustment. 4.5 Multivariate Tests of Hypotheses. 4.6 Tests of Homogeneity. 4.7 Efficient Tests of No Partial Association. 4.8 Asymptotic Relative Efficiency of Competing Tests. 4.9 MaximinEfficient Robust Tests. 4.10 Random Effects Model. 4.11 Power and Sample Size for Tests of Association. 4.12 Polychotomous and Ordinal Data. 4.13 Problems.
 5 CaseControl and Matched Studies. 5.1 Unmatched CaseControl (Retrospective) Sampling. 5.2 Matching. 5.3 Tests of Association for Matched Pairs. 5.4 Measures of Association for Matched Pairs. 5.5 PairMatched Retrospective Study. 5.6 Power Function of McNemar's Test. 5.7 Stratified Analysis of PairMatched Tables. 5.8 Multiple MatchingMantelHaenszel Analysis. 5.9 Matched Polychotomous Data. 5.10 Kappa Index of Agreement. 5.11 Problems.
 6 Applications of Maximum Likelihood and Efficient Scores. 6.1 Binomial. 6.2 2x2 Table: Product Binomial (Unconditionally). 6.3 2x2 Table, Conditionally. 6.4 ScoreBased Estimate. 6.5 Stratified Score Analysis of Independent 2x2 Tables. 6.6 Matched Pairs. 6.7 Iterative Maximum Likelihood. 6.8 Problems.
 7 Logistic Regression Models. 7.1 Unconditional Logistic Regression Model. 7.2 Interpretation of the Logistic Regression Model. 7.3 Tests of Significance. 7.4 Interactions. 7.5 Measures of the Strength of Association. 7.6 Conditional Logistic Regression Model for Matched Sets. 7.7 Models for Polychotomous or Ordinal Data. 7.8 Random Effects and Mixed Models. 7.9 Models for Multivariate or Repeated Measures. 7.10 Problems.
 8 Analysis of Count Data. 8.1 Event Rates and the Homogeneous Poisson Model. 8.2 Over Dispersed Poisson Model. 8.3 Poisson Regression Model. 8.4 Over Dispersed and Robust Poisson Regression. 8.5 Conditional Poisson Regression for Matched Sets. 8.6 Negative Binomial Models. 8.7 Power and Sample Size. 8.8 Multiple Outcomes. 8.9 Problems.
 9 Analysis of EventTime Data. 9.1 Introduction to Survival Analysis. 9.2 Lifetable Construction. 9.3 Family of Weighted MantelHaenszel Tests. 9.4 Proportional Hazards Models. 9.5 Evaluation of Sample Size and Power. 9.6 Additional Models. 9.7 Analysis of Recurrent Events. 9.8 Problems. Appendix Statistical Theory. A.1 Introduction. A.2 Central Limit Theorem and the Law of Large Numbers. A.3 Delta Method. A.4 Slutsky's Convergence Theorem. A.5 Least Squares Estimation. A.6 Maximum Likelihood Estimation and Efficient Scores. A.7 Tests of Significance. A.8 Explained Variation. A.9 Robust Inference. A.10 Generalized Linear Models and QuasiLikelihood. A.11 Generalized Estimating Equations (GEE). References. Author Index. Subject Index.
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 VanPool, Todd L., 1968
 Malden, MA : WileyBlackwell, 2011.
 Description
 Book — 1 online resource (xxii, 350 pages)
 Summary

 List of Tables. List of Figures. List of Equations. Acknowledgments.
 1 Quantifying Archaeology.
 2 Data. Scales of Measurement. Nominal level measurement. Ordinal level measurement. Interval level measurement. Ratio level measurement. The relationship among the scales of measurement. Validity. Accuracy and Precision. Populations and Samples.
 3 Characterizing Data Visually. Frequency Distributions. Histograms. Stem and Leaf Diagrams. Ogives (Cumulative Frequency Distributions). Describing a Distribution. Bar Charts. Displaying Data like a Pro. Archaeology and Exploratory Data Analysis.
 4 Characterizing Data Numerically: Descriptive Statistics. Measures of Central Tendency. Mean. Median. Mode. Which measure of location is best? Measures of Dispersion. Range. Interquartile range. Variance and standard deviation. Calculating Estimates of the Mean and Standard Deviation. Coefficients of Variation. Box Plots. Characterizing Nominal and Ordinal Scale Data. Index of dispersion for nominal data and the index of qualitative variation.
 5 An Introduction to Probability. Theoretical Determinations of Probability. Empirical Determinations of Probability. Complex Events. Using Probability to Determine Likelihood. The Binomial Distribution. The psychic's trick. Simplifying the binomial. Probability in Archaeological Contexts.
 6 Putting Statistics to Work: The Normal Distribution.
 7 Hypothesis Testing I: An Introduction. Hypotheses of Interest. Formal Hypothesis Testing and the Null Hypothesis. Errors in Hypothesis Testing.
 8 Hypothesis Testing II: Confi dence Limits, the tDistribution, and OneTailed Tests. Standard Error. Comparing Sample Means to m. Statistical Inference and Confidence Limits. The tDistribution. Degrees of freedom and the tdistribution. Hypothesis Testing Using the tDistribution. Testing OneTailed Null Hypotheses.
 9 Hypothesis Testing III: Power. Calculating. Statistical Power. Increasing the power of a test. Calculating Power: An Archaeological Example. Power Curves. Putting it all Together: A Final Overview of Hypothesis Testing. Steps to hypothesis testing. Evaluating common hypotheses.
 10 Analysis of Variance and the FDistribution. Model II ANOVA: Identifying the Impacts of Random Effects. Model I ANOVA: The Analysis of Treatment Effects. A Final Summary of Model I and Model II ANOVA. ANOVA Calculation Procedure. Identifying the Sources of Signifi cant Variation in Model I and Model II ANOVA. Comparing Variances.
 11 Linear Regression and Multivariate Analysis. Constructing a Regression Equation. Evaluating the Statistical Significance of Regression. Using Regression Analysis to Predict Values. Placing confi dence intervals around the regression coefficient. Confidence Limits around Y for a Given Xi. Estimating X from Y. The Analysis of Residuals. Some Final Thoughts about Regression. Selecting the right regression model. Do not extrapolate beyond the boundaries of the observed data. Use the right methods when creating reverse predictions. Be aware of the assumptions for regression analysis. You may be able to transform your data to create a linear relationship from a curvilinear relationship. Use the right confi dence limits.
 12 Correlation. Pearson s ProductMoment Correlation Coefficient. The assumptions of Pearson's productmoment correlation coeffi cient. Spearman's Rank Order Correlation Coeffi cient. Some Final Thoughts (and Warnings) about Correlation.
 13 Analysis of Frequencies. Determining the Source of Variation in a ChiSquare Matrix. Assumptions of ChiSquare Analysis. The Analysis of Small Samples Using Fisher s Exact Test and Yate's Continuity Correction. The Median Test.
 14 An Abbreviated Introduction to Nonparametric and Multivariate Analysis. Nonparametric Tests Comparing Groups. Wilcoxon twosample test. Kruskal Wallis nonparametric ANOVA. Multivariate Analysis and the Comparison of Means. A review of pertinent conceptual issues. Twoway ANOVA. Nested ANOVA.
 15 Factor Analysis and Principal Component Analysis. Objectives of Principal Component and Factor Analysis. Designing the Principal Component/Factor Analysis. Assumptions and Conceptual Considerations of Factor Analysis. An Example of Factor Analysis. Factor Analysis vs. Principal Component Analysis.
 16 Sampling, Research Designs, and the Archaeological Record. How to Select a Sample. How Big a Sample is Necessary? Some Concluding Thoughts. References. Appendix A Areas under a Standardized Normal Distribution. Appendix B Critical Values for the Student's tDistribution. Appendix C Critical Values for the FDistribution. Appendix D Critical Values for the ChiSquare Distribution. Appendix E Critical Values for the Wilcoxon TwoSample UTest. Index.
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 Zhou, XiaoHua.
 2nd ed.  Hoboken, N.J. : Wiley, ©2011.
 Description
 Book — 1 online resource (xxx, 545 pages) : illustrations.
 Summary

 List of Figures xix List of Tables xxiii 0.1 Preface xxix 0.2 Acknowledgements xxx Part I. Basic Concepts and Methods
 1. Introduction
 3 1.1 Diagnostic Test Accuracy Studies
 3 1.2 Case Studies
 6 1.3 Software
 10 1.4 Topics Not Covered in This Book
 10
 2. Measures of Diagnostic Accuracy
 13 2.1 Sensitivity and Specificity
 14 2.2 Combined Measures of Sensitivity and Specificity
 21 2.3 Receiver Operating Characteristic (ROC) Curve
 24 2.4 Area Under the ROC Curve
 27 2.5 Sensitivity at Fixed EPR
 34 2.6 Partial Area Under the ROC Curve
 35 2.7 Likelihood Ratios
 36 2.8 ROC Analysis When the True Diagnosis Is not Binary
 41 2.9 CStatistics and Other Measures to Compare Prediction Models
 43 2.10 Detection and Localization of Multiple Lesions
 44 2.11 Positive and Negative Predictive Values, Bayes Theorem, and Case Study
 2
 47 2.12 Optimal Decision Threshold on the ROC Curve
 51 2.13 Interpreting the Results of Multiple Tests
 54
 3. Design of Diagnostic Accuracy Studies
 57 3.1 Establish the Objective of the Study
 58 3.2 Identify the Target Patient Population
 63 3.3 Select a Sampling Plan for Patients
 64 3.4 Select the Gold Standard
 72 3.5 Choose A Measure of Accuracy
 79 3.6 Identify Target Reader Population
 82 3.7 Select Sampling Plan for Readers
 83 3.8 Plan Data Collection
 84 3.9 Plan Data Analyses
 94 3.10 Determine Sample Size
 101
 4. Estimation and Hypothesis Testing in a Single Sample
 103 4.1 BinaryScale Data
 104 4.2 OrdinalScale Data
 117 4.3 ContinuousScale Data
 141 4.4 Testing the Hypothesis that the ROC Curve Area or Partial Area Is a Specific Value
 163
 5. Comparing the Accuracy of Two Diagnostic Tests
 165 5.1 BinaryScale Data
 166 5.2 Ordinal and ContinuousScale Data
 174 5.3 Tests of Equivalence
 189
 6. Sample Size Calculations
 193 6.1 Studies Estimating the Accuracy of a Single Test
 194 6.2 Sample Size for Detecting a Difference in Accuracies of Two Tests
 203 6.3 Sample Size for Assessing NonInferiority of Equivalency of Two Tests
 214 6.4 Sample Size for Determining a Suitable Cutoff Value
 218 6.5 Sample Size Determination for MultiReader Studies
 219 6.6 Alternative to Sample Size Formulae
 228
 7. Introduction to Metaanalysis for Diagnostic Accuracy Studies
 231 7.1 Objectives
 232 7.2 Retrieval of the Literature
 233 7.3 Inclusion/Exclusion Criteria
 237 7.4 Extracting Information from the Literature
 241 7.5 Statistical Analysis
 243 7.6 Public Presentation
 258 Part II. Advanced Methods
 8. Regression Analysis for Independent ROC Data
 263 8.1 Four Clinical Studies
 264 8.2 Regression Models for ContinuousScale Tests
 267 8.3 Regression Models for OrdinalScale Tests
 287 8.4 Covariate Adjusted ROC Curves of ContinuousScale tests
 294
 9. Analysis of Multiple Reader and/or Multiple Test Studies
 297 9.1 Studies Comparing Multiple Tests with Covariates
 298 9.2 Studies with Multiple Readers and Multiple Tests
 310 9.3 Analysis of Multiple Tests Designed to Locate and Diagnose Lesions
 325
 10. Methods for Correcting Verification Bias
 329 10.1 Examples
 330 10.2 Impact of Verification Bias
 333 10.3 A Single BinaryScale Test
 334 10.4 Correlated BinaryScale Tests
 341 10.5 A Single OrdinalScale Test
 348 10.6 Correlated OrdinalScale Tests
 360 10.7 ContinuousScale Tests
 372
 11. Methods for Correcting Imperfect Gold Standard Bias
 389 11.1 Examples
 390 11.2 Impact of Imperfect Gold Standard Bias
 393 11.3 One Single Binary test in a Single Population
 395 11.4 One Single Binary test in G Populations
 402 11.5 Multiple Binary Tests in One Single Population
 408 11.6 Multiple Binary Tests in G Populations
 423 11.7 Multiple OrdinalScale Tests in One Single Population
 425 11.8 MultipleScale Tests in One Single Population
 429
 12. Statistical Analysis for Metaanalysis
 435 12.1 BinaryScale Data
 436 12.2 Ordinal or ContinuousScale Data
 438 12.3 ROC Curve Area
 445 Appendix A. Case Studies and
 Chapter 8 Data
 449 Appendix B. Jackknife and Bootstrap Methods of Estimating Variances and Confidence Intervals 477.
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14. Data analysis in forensic science [electronic resource] : a Bayesian decision perspective [2010]
 Chichester : John Wiley and Sons, c2010.
 Description
 Book — 1 online resource (1 electronic resource (xvii, 369 p.))
 Summary

 Foreword. Preface. I The Foundations of Inference and Decision in Forensic Science .
 1 Introduction . 1.1 The Inevitability of Uncertainty. 1.2 Desiderata in Evidential Assessment. 1.3 The Importance of the Propositional Framework and the Nature of Evidential Assessment. 1.4 From Desiderata to Applications. 1.5 The Bayesian Core of Forensic Science. 1.6 Structure of the Book.
 2 Scientific Reasoning and Decision Making. 2.1 Coherent Reasoning Under Uncertainty. 2.2 Coherent Decision Making Under Uncertainty of Reasoning. 2.3 Scientific Reasoning as Coherent Decision Making. 2.4 Forensic Reasoning as Coherent Decision Making.
 3 Concepts of Statistical Science and Decision Theory. 3.1 Random Variables and Distribution Functions. 3.2 Statistical Inference and Decision Theory. 3.3 The Bayesian Paradigm. 3.4 Bayesian Decision Theory. 3.5 R Code. II Forensic Data Analysis.
 4 Point Estimation. 4.1 Introduction. 4.2 Bayesian Decision for a Proportion. 4.3 Bayesian Decision for a Poisson Mean. 4.4 Bayesian Decision for Normal Mean. 4.5 R Code.
 5 Credible Intervals. 5.1 Introduction. 5.2 Credible Intervals. 5.3 DecisionTheoretic Evaluation of Credible Intervals. 5.4 R Code.
 6 Hypothesis Testing. 6.1 Introduction. 6.2 Bayesian Hypothesis Testing. 6.3 Onesided testing. 6.4 TwoSided Testing. 6.5 R Code.
 7 Sampling. 7.1 Introduction. 7.2 Sampling Inspection. 7.3 Graphical Models for Sampling Inspection. 7.4 Sampling Inspection under a DecisionTheoretic Approach. 7.5 R Code.
 8 Classification of Observations. 8.1 Introduction. 8.2 Standards of Coherent Classification. 8.3 Comparing Models using Discrete Data. 8.4 Comparison of Models using Continuous Data. 8.5 NonNormal Distributions and Cocaine on Bank Notes. 8.6 A note on Multivariate Continuous Data. 8.7 R Code.
 9 Bayesian Forensic Data Analysis: Conclusions and Implications. 9.1 Introduction. 9.2 What is the Past and Current Position of Statistics in Forensic Science? 9.3 Why Should Forensic Scientists Conform to a Bayesian Framework for Inference and Decision Making? 9.4 Why Regard Probability as a Personal Degree of Belief? 9.5 Why Should Scientists be Aware of Decision Analysis? 9.6 How to Implement Bayesian Inference and Decision Analysis? A Discrete Distributions. B Continuous Distributions. Bibliography. Author Index. Subject Index.
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 Senn, Stephen.
 2nd ed.  Chichester, England ; Hoboken, NJ : John Wiley & Sons, c2007.
 Description
 Book — xix, 498 p. : ill. ; 25 cm.
 Summary

 Preface to the Second Edition. Preface to the First Edition. Acknowledgements.
 1. Introduction. 1.1 Drug development. 1.2 The role of statistics in drug development. 1.3 The object of this book. 1.4 The author's knowledge of statistics in drug development. 1.5 The reader and his or her knowledge of statistics. 1.6 How to use the book.
 Part 1: Four Views of Statistics in Drug Development: Historical, Methodological, Technical and Professional.
 2. A Brief and Superficial History of Statistics for Drug Developers. 2.1 Introduction. 2.2 Early Probabilists. 2.3 James Bernoulli (16541705). 2.4 John Arbuthnott (16671753). 2.5 The mathematics of probability in the late 17th, the 18th and early 19th centuries. 2.6 Thomas Bayes (17011761). 2.7 Adolphe Quetelet (17961874). 2.8 Francis Galton (18221911). 2.9 Karl Pearson (18571936). 2.10 'Student' (18761937). 2.11 R.A. Fisher (18901962). 2.12 Modern mathematical statistics. 2.13 Medical statistics. 2.14 Statistics in clinical trials today. 2.15 The current debate. 2.16 A living science. 2.17 Further reading.
 3. Design and Interpretation of Clinical Trials as Seen by a Statistician. 3.1 Prefatory warning. 3.2 Introduction. 3.3 Defining effects. 3.4 Practical problems in using the counterfactual argument. 3.5 Regression to the mean. 3.6 Control in clinical trials. 3.7 Randomization. 3.8 Blinding. 3.9 Using concomitant observations. 3.10 Measuring treatment effects. 3.11 Data generation models. 3.12 In conclusion. 3.13 Further reading.
 4. Probability, Bayes, Pvalues, Tests of Hypotheses and Confidence Intervals. 4.1 Introduction. 4.2 An example. 4.3 Odds and sods. 4.4 The Bayesian solution to the example. 4.5 Why don't we regularly use the Bayesian approach in clinical trials? 4.6 A frequentist approach. 4.7 Hypothesis testing in controlled clinical trials. 4.8 Significance tests and Pvalues. 4.9 Confidence intervals and limits and credible intervals. 4.10 Some Bayesian criticism of the frequentist approach. 4.11 Decision theory. 4.12 Conclusion. 4.13 Further reading .
 5. The Work of the Pharmaceutical Statistician. 5.1 Prefatory remarks. 5.2 Introduction. 5.3 In the beginning. 5.4 The trial protocol. 5.5 The statistician's role in planning the protocol. 5.6 Sample size determination. 5.7 Other important design issues. 5.8 Randomization. 5.9 Data collection preview. 5.10 Performing the trial. 5.11 Data analysis preview. 5.12 Analysis and reporting. 5.13 Other activities. 5.14 Statistical research. 5.15 Further reading.
 Part 2: Statistical Issues: Debatable and Controversial Topics in Drug Development.
 6. Allocating Treatments to Patients in Clinical Trials. 6.1 Background. 6.2 Issues. References. 6.A Technical appendix.
 7. Baselines and Covariate Information. 7.1 Background. 7.2 Issues. 7.A Technical appendix.
 8. The Measurement of Treatment Effects. 8.1 Background. 8.2 Issues. 8.A Technical appendix.
 9. Demographic Subgroups: Representation and Analysis. 9.1 Background. 9.2 Issues. 9.A Technical appendix.
 10. Multiplicity. 10.1 Background. 10.2 Issues. 10.A Technical appendix.
 11. Intention to Treat, Missing Data and Related Matters. 11.1 Background. 11.2 Issues. 11.A Technical appendix.
 12. Onesided and Twosided Tests and Other Issues to Do with Significance and Pvalues. 12.1 Background. 12.2 Issues.
 13. Determining the Sample Size. 13.1 Background. 13.2 Issues.
 14. Multicentre Trials. 14.1 Background. 14.2 Issues. 14.A Technical appendix.
 15. Active Control Equivalence Studies. 15.1 Background. 15.2 Issues. 15.A Technical appendix.
 16. MetaAnalysis. 16.1 Background. 16.2 Issues. 16.A Technical appendix.
 17. Crossover Trials. 17.1 Background. 17.2 Issues.
 18. nof1 Trials. 18.1 Background. 18.2 Issues.
 19. Sequential Tr4ials. 19.1 Background. 19.2 Issues.
 20. Dosefinding. 20.1 Background. 20.2 Issues.
 21. Concerning Pharmacokinetics and Pharmacodynamics. 21.1 Background. 21.2 Issues.
 22. Bioequivalence Studies. 22.1 Background. 22.2 Issues.
 23. Safety Data, Harms, Drug Monitoring and Pharmacoepidemiology. 23.1 Background. 23.2 Issues.
 24. Pharmacoeconomics and Portfolio Management. 24.1 Background. 24.2 Issues.
 25. Concerning Pharmacogenetics, Pharmacogenomics and Related Matters. 25.1 Background. 25.2 Issues. 25.A Technical appendix. Glossary. Index.
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 Chandler, R. E. (Richard E.), author.
 Hoboken, N.J. : Wiley, 2011.
 Description
 Book — 1 online resource (371 pages).
 Summary

 Preface. Contributing authors. Part I METHODOLOGY.
 1 Introduction. 1.1 What is a trend? 1.2 Why analyse trends? 1.3 Some simple examples. 1.4 Considerations and Difficulties. 1.5 Scope of the book. 1.6 Further reading. References.
 2 Exploratory analysis. 2.1 Data visualisation. 2.2 Simple smoothing. 2.3 Linear filters. 2.4 Classical test procedures. 2.5 Concluding comments. References.
 3 Parametric modelling  deterministic trends. 3.1 The Linear trend. 3.2 Multiple regression techniques. 3.3 Violations of assumptions. 3.4 Nonlinear trends. 3.5 Generalized linear models. 3.6 Inference with small samples. References.
 4 Nonparametric trend estimation. 4.1 An introduction to nonparametric regression. 4.2 Multiple covariates. 4.3 Other nonparametric estimation techniques. 4.4 Parametric or nonparametric? References.
 5 Stochastic trends. 5.1 Stationary time series models and their properties. 5.2 Trend removal via differencing. 5.3 Long memory models. 5.4 Models for irregularly spaced series. 5.5 State space and structural models. 5.6 Nonlinear models. References.
 6 Other issues. 6.1 Multisite data. 6.2 Multivariate series. 6.3 Point process data. 6.4 Trends in extremes. 6.5 Censored data. References. Part II CASE STUDIES.
 7 Additive models for sulphur dioxide pollution in Europe (Marco Giannitrapani, Adrian Bowman, E. Marian Scott and Ron Smith) 7.1 Introduction. 7.2 Additive models with correlated errors. 7.3 Models for the SO2 data. 7.4 Conclusions. References.
 8 Rainfall trends in southwest Western Australia (Richard E. Chandler, Bryson C. Bates and Stephen P. Charles). 8.1 Motivation. 8.2 The study region. 8.3 Data used in the study. 8.4 Modelling methodology. 8.5 Results. 8.6 Summary and conclusions. References.
 9 Estimation of Common tends for tropical index series (Alain F. Zuur, Elena N. Ieno, Christina Mazziotti, Giuseppe Montanari, Attilio Rinaldi and Carla Rita Ferrari). 9.1 Introduction. 9.2 Data exploration. 9.3 Common trends and additive modelling. 9.4 Dynamic factor analysis to estimate common trends. 9.5 Discussion. Acknowledgement. References.
 10 A Spacetime study on forest health (Thomas Kneib and Ludwig Fahrmeir). 10.1 Forest health: survey and data. 10.2 Regression models for longitudinal data with ordinal responses. 10.3 Spatiotemporal models. 10.4 Spatiotemporal modelling and analysis of forest health data. Acknowledgements. References. Index.
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(source: Nielsen Book Data)
 Willan, Andrew Roger, 1947
 Chichester, England ; Hoboken, NJ : John Wiley, c2006.
 Description
 Book — xii, 196 p. : ill. ; 24 cm.
 Summary

 Preface.
 1. Concepts. 1.1 Introduction. 1.2 Costeffectiveness data and the parameters of interest. 1.3 The costeffectiveness plane, the ICER and INB. 1.4 Outline.
 2. Parameter Estimation for Noncensored Data. 2.1 Introduction. 2.2 Cost. 2.3 Effectiveness. 2.4 Summary.
 3. Parameter Estimation for Censored Data. 3.1 Introduction. 3.2 Mean Cost. 3.3 Effectiveness. 3.4 Summary.
 4. Costeffectiveness Analysis. 4.1 Introduction. 4.2 Incremental costeffectiveness ratio. 4.3 Incremental net benefit. 4.4 The costeffectiveness acceptability curve. 4.5 Using bootstrap methods. 4.6 A Bayesian incremental net benefit approach. 4.7 Kinked thresholds. 4.8 Summary.
 5. Costeffectiveness Analysis: Examples. 5.1 Introduction. 5.2 The CADETHp trial. 5.3 Symptomatic hormoneresistant prostate cancer. 5.4 The Canadian implantable defibrillator study (CIDS). 5.5 The EVALUATE trial. 5.6 Bayesian approach applied to the UK PDS study. 5.7 Summary.
 6. Power and Sample Size Determination. 6.1 Introduction. 6.2 Approaches based on the costeffectiveness plane. 6.3 The classical approach based on net benefit. 6.4 Bayesian take on the classical approach. 6.5 The value of information approach. 6.6 Summary.
 7. Covariate Adjustment and Subgroup Analysis. 7.1 Introduction. 7.2 Noncensored data. 7.3 Censored data. 7.4 Summary.
 8. Multicenter and Multinational Trials. 8.1 Introduction. 8.2 Background to multinational costeffectiveness. 8.3 Fixed effect approaches. 8.4 Random effects approaches. 8.5 Summary.
 9. Modeling Costeffectiveness. 9.1 Introduction. 9.2 A general framework for modeling costeffectiveness results. 9.3 Case study: an economic appraisal of the goal study. 9.4 Summary. References. Author Index. Subject Index. Series List.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Online

 dx.doi.org Wiley Online Library
 Google Books (Full view)
 Middleton, David, 1920
 1st ed.  Hoboken, N.J. : WileyIEEE Press, 2012.
 Description
 Book — 1 online resource.
 Summary

 Reception as a Statistical Decision Problem
 SpaceTime Covariances and Wave Number Frequency Spectra: I. Noise and Signals with Continuous and Discrete Sampling
 Optimum Detection, SpaceTime Matched Filters, and Beam Forming in Gaussian Noise Fields
 Multiple Alternative Detection
 Bayes Extraction Systems: Signal Estimation and Analysis, () = 1
 Joint Detection and Estimation, () = 1: I. Foundations
 Joint Detection and Estimation under Uncertainty, () <1. II. Multiple Hypotheses and Sequential Observations
 The Canonical Channel I: Scalar Field Propagation in a Deterministic Medium
 The Canonical Channel II: Scattering in Random Media; 'Classical' Operator Solutions.
(source: Nielsen Book Data)
 Online

 dx.doi.org Wiley Online Library
 Google Books (Full view)
 Middleton, David, 1920
 1st ed.  Hoboken, N.J. : WileyIEEE Press, 2012.
 Description
 Book — 1 online resource.
 Summary

 Reception as a Statistical Decision Problem
 SpaceTime Covariances and Wave Number Frequency Spectra: I. Noise and Signals with Continuous and Discrete Sampling
 Optimum Detection, SpaceTime Matched Filters, and Beam Forming in Gaussian Noise Fields
 Multiple Alternative Detection
 Bayes Extraction Systems: Signal Estimation and Analysis, () = 1
 Joint Detection and Estimation, () = 1: I. Foundations
 Joint Detection and Estimation under Uncertainty, () <1. II. Multiple Hypotheses and Sequential Observations
 The Canonical Channel I: Scalar Field Propagation in a Deterministic Medium
 The Canonical Channel II: Scattering in Random Media; 'Classical' Operator Solutions.
(source: Nielsen Book Data)
 Yin, Guosheng, author.
 Hoboken, N.J. : John Wiley & Sons, ©2012.
 Description
 Book — 1 online resource (xvii, 336 pages) : illustrations. Digital: text file.
 Summary

 Preface xv
 1. Introduction
 1 1.1 What Are Clinical Trials?
 1 1.2 Brief History and Adaptive Designs
 3 1.3 Modern Clinical Trials
 7 1.4 Different Types of Drugs
 12 1.5 New Drug Development
 13 1.6 Emerging Challenges
 16 1.7 Summary
 17
 2. Fundamentals of Clinical Trials
 21 2.1 Key Components of Clinical Trials
 21 2.2 Pharmacokinetics and Pharmacodynamics
 35 2.3 Phases IIV of Clinical Trials
 38 2.4 Summary
 42
 3. Frequentist versus Bayesian Statistics
 45 3.1 Basic Statistics
 45 3.2 Frequentist Estimation and Inference
 62 3.3 Survival Analysis
 77 3.4 Bayesian Methods
 86 3.5 Markov Chain Monte Carlo
 105 3.6 Summary
 109
 4. Phase I Trial Design
 113 4.1 Maximum Tolerated Dose
 113 4.2 Initial Dose and Spacing
 116 4.3
 3 +
 3 Design
 120 4.4 A + B Design
 125 4.5 Accelerated Titration Design
 126 4.6 Biased Coin DoseFinding Method
 130 4.7 Continual Reassessment Method
 132 4.8 Bayesian Model Averaging Continual Reassessment Method
 140 4.9 Escalation with Overdose Control
 152 4.10 Bayesian Hybrid Design Using Bayes Factor
 155 4.11 Summary
 162
 5. Phase II Trial Design
 169 5.1 Gehan s TwoStage Design
 173 5.2 Simon s TwoStage Design
 175 5.3 Bayesian Posterior Probability Monitoring
 179 5.4 Bayesian Predictive Probability Monitoring
 183 5.5 Predictive Monitoring in Randomized Phase II Trials
 186 5.6 Predictive Probability with Adaptive Randomization
 191 5.7 Phase II Design with Multiple Outcomes
 198 5.8 Phase I/II Design with Bivariate Binary Data
 206 5.9 Phase I/II Design with Times to Toxicity and Efficacy
 218 5.10 Summary
 229
 6. Phase III Trial Design
 233 6.1 Power and Sample Size
 233 6.2 Comparing Means for Continuous Outcomes
 240 6.3 Comparing Proportions for Binary Outcomes
 252 6.4 Sample Size with Survival Data
 262 6.5 Sample Size for Correlated Data
 270 6.6 Group Sequential Methods
 274 6.7 Adaptive Designs
 297 6.8 Causality and Noncompliance
 310 6.9 Phase IV PostApproval Trial
 317
 7. Adaptive Randomization
 323 7.1 Introduction
 323 7.2 Simple Randomization
 326 7.3 Permuted Block Randomization
 327 7.4 Stratified Randomization
 328 7.5 CovariateAdaptive Allocation by Minimization
 329 7.6 Biased Coin Design
 333 7.7 PlaytheWinner Rule
 335 7.8 DroptheLoser Rule
 338 7.9 Optimal Adaptive Randomization
 339 7.10 Doubly Adaptive Biased Coin Design
 346 7.11 Bayesian Adaptive Randomization
 347 7.12 Adaptive Randomization with Efficacy and Toxicity Tradeoffs
 356 7.13 Fixed or Adaptive Randomization?
 360
 8. LateOnset Toxicity
 367 8.1 Introduction
 367 8.2 Missing Data Caused by Delayed Outcomes
 369 8.3 Fractional
 3 +
 3 Design
 371 8.4 Fractional Continual Reassessment Method
 377 8.5 TimetoEvent Continual Reassessment Method
 379 8.6 EM Continual Reassessment Method
 382
 9. DrugCombination Trials
 393 9.1 Why Are Drugs Combined?
 393 9.2 New Challenges
 397 9.3 Sequential DoseFinding Scheme
 402 9.4 Dose Finding with CopulaType Regression
 405 9.5 Latent Contingency Table Approach
 414 9.6 Combination of Discrete and Continuous Doses
 419 9.7 Phase I/II DrugCombination Design
 426 9.8 Summary
 434
 10. Targeted Therapy Design
 437 10.1 Cytostatic Agent
 437 10.2 Prognostic and Predictive Biomarkers
 439 10.3 Predictive Biomarker Validation
 441 10.4 Randomized Discontinuation Design
 444 10.5 Adaptive Signature Design
 447
 10.6 Adaptive Threshold Design
 451 References
 457 Author Index
 476 Subject Index 480.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
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