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Book
xv, 393 pages : illustrations ; 27 cm.
  • Simple linear regression
  • Multiple linear regression
  • Regression diagnostics : detection of model violations
  • Qualitative variables as predictors
  • Transformation of variables
  • Weighted least squares
  • The problem of correlated errors
  • Analysis of collinear data
  • Working with collinear data
  • Variable selection procedures
  • Logistic regression
  • Further topics.
Praise for the Fourth Edition: "This book is ...an excellent source of examples for regression analysis. It has been and still is readily readable and understandable." -Journal of the American Statistical Association Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. Regression Analysis by Example, Fifth Edition has been expanded and thoroughly updated to reflect recent advances in the field. The emphasis continues to be on exploratory data analysis rather than statistical theory. The book offers in-depth treatment of regression diagnostics, transformation, multicollinearity, logistic regression, and robust regression. The book now includes a new chapter on the detection and correction of multicollinearity, while also showcasing the use of the discussed methods on newly added data sets from the fields of engineering, medicine, and business. The Fifth Edition also explores additional topics, including: * Surrogate ridge regression * Fitting nonlinear models * Errors in variables * ANOVA for designed experiments Methods of regression analysis are clearly demonstrated, and examples containing the types of irregularities commonly encountered in the real world are provided. Each example isolates one or two techniques and features detailed discussions, the required assumptions, and the evaluated success of each technique. Additionally, methods described throughout the book can be carried out with most of the currently available statistical software packages, such as the software package R. Regression Analysis by Example, Fifth Edition is suitable for anyone with an understanding of elementary statistics.
(source: Nielsen Book Data)9780470905845 20160619
Law Library (Crown)
LAW-362-01
Book
xi, 573 p. : ill. ; 25 cm.
  • Preface.Chapter 1 Introduction: The Bayesian Method, its Benefits and Implementation.Chapter 2 Bayesian Model Choice, Comparison and Checking.Chapter 3 The Major Densities and their Application.Chapter 4 Normal Linear Regression, General Linear Models and Log-Linear Models.Chapter 5 Hierarchical Priors for Pooling Strength and Overdispersed Regression Modelling.Chapter 6 Discrete Mixture Priors.Chapter 7 Multinomial and Ordinal Regression Models.Chapter 8 Time Series Models.Chapter 9 Modelling Spatial Dependencies.Chapter 10 Nonlinear and Nonparametric Regression.Chapter 11 Multilevel and Panel Data Models.Chapter 12 Latent Variable and Structural Equation Models for Multivariate Data.Chapter 13 Survival and Event History Analysis.Chapter 14 Missing Data Models.Chapter 15 Measurement Error, Seemingly Unrelated Regressions, and Simultaneous Equations.Appendix 1 A Brief Guide to Using WINBUGS.Index.
  • (source: Nielsen Book Data)9780470018750 20160528
Bayesian methods combine the evidence from the data at hand with previous quantitative knowledge to analyse practical problems in a wide range of areas. The calculations were previously complex, but it is now possible to routinely apply Bayesian methods due to advances in computing technology and the use of new sampling methods for estimating parameters. Such developments together with the availability of freeware such as 'WinBugs' and 'R' have facilitated a rapid growth in the use of Bayesian methods, allowing their application in many scientific disciplines, including applied statistics, public health research, medical science, the social sciences and economics. Following the success of the first edition, this reworked and updated book provides an accessible approach to Bayesian computing and analysis, with an emphasis on the principles of prior selection, identification and the interpretation of real data sets. This second edition: provides an integrated presentation of theory, examples, applications and computer algorithms; discusses the role of Markov Chain Monte Carlo methods in computing and estimation; includes a wide range of interdisciplinary applications, and a large selection of worked examples from the health and social sciences; features a comprehensive range of methodologies and modelling techniques, and examines model fitting in practice using Bayesian principles; and, provides exercises designed to help reinforce the reader's knowledge and a supplementary website containing data sets and relevant programs. "Bayesian Statistical Modelling" is ideal for researchers in applied statistics, medical science, public health and the social sciences, who will benefit greatly from the examples and applications featured. This book will also appeal to graduate students of applied statistics, data analysis and Bayesian methods, and will provide a great source of reference for both researchers and students. Praise for the first edition: 'It is a remarkable achievement to have carried out such a range of analysis on such a range of data sets. I found this book comprehensive and stimulating, and was thoroughly impressed with both the depth and the range of the discussions it contains' - "ISI: Short Book Reviews". 'This is an excellent introductory book on Bayesian modelling techniques and data analysis' - "Biometrics". 'The book fills an important niche in the statistical literature and should be a very valuable resource for students and professionals who are utilizing Bayesian methods' - "Journal of Mathematical Psychology".
(source: Nielsen Book Data)9780470018750 20160528
Law Library (Crown)
LAW-362-01

3. Mastering MATLAB 7 [2005]

Book
xi, 852 p. : ill. ; 24 cm.
  • 1. Getting Started. 2. Basic Features. 3. The MATLAB Desktop. 4. Script M-Files. 5. Arrays and Array Operations. 6. Multidimensional Arrays. 7. Cell Arrays and Structures. 8. Character Strings. 9. Relational and Logical Operations. 10. Control Flow. 11. Function M-Files. 12. M-File Debugging and Profiling. 13. File and Directory Management. 14. Set, Bit, and Base Functions. 15. Time Computations. 16. Matrix Algebra. 17. Data Analysis. 18. Data Interpolation. 19. Polynomials. 20. Cubic Splines. 21. Fourier Analysis. 22. Optimization. 23. Integration and Differentiation. 24. Differential Equations. 25. Two-Dimensional Graphics. 26. Three-Dimensional Graphics. 27. Using Color and Light. 28. Images, Movies, and Sound. 29. Printing and Exporting Graphics. 30. Handle Graphics. 31. Graphical User Interfaces. 32. Dialog Boxes. 33. MATLAB Classes and Object-Oriented Programming. 34. MATLAB Programming Interfaces. 35. Extending MATLAB with Java. 36. Windows Application Integration. 37. Getting Help. 38. Examples, Examples, Examples. Appendix A: Common Handle Graphics Properties. Appendix B: Axes Object Properties. Appendix C: Figure Object Properties. Appendix D: Image Object Properties. Appendix E: Light Object Properties. Appendix F: Line Object Properties. Appendix G: Patch Object Properties. Appendix H: Rectangle Object Properties. Appendix I: Root Object Properties. Appendix J: Surface Object Properties. Appendix K: Text Object Properties. Appendix L: Uicontextmenu and Uimenu Object Properties. Appendix M: Uicontrol Object Properties. Index.
  • (source: Nielsen Book Data)9780131857148 20160527
For undergraduate and graduate courses in MATLAB or as a reference in courses where MATLAB is used. This text covers all essential aspects of MATLAB presented within an easy-to-follow 'learn while doing' tutorial format. All new features of the latest release of MATLAB are covered-Demonstrates how the new features can be used to increase productivity and do new things Discusses integration of MATLAB with C, FORTRAN, AND Java-Increases MATLAB's power and flexibility in dealing with external algorithms, datasets, and operating system capabilities Thorough coverage of indexing, vectorizing, and linear algebra - increases students' problem-solving productivity and dramatically increases algorithm speed Abundant examples throughout - Also includes a chapter that specifically covers extensive examples Comprehensive index.
(source: Nielsen Book Data)9780131857148 20160527
For undergraduate and graduate courses in MATLAB or as a reference in courses where MATLAB is used. This text covers all essential aspects of MATLAB presented within an easy- to-follow "learn while doing" tutorial format.
(source: Nielsen Book Data)9780131430181 20160527
Law Library (Crown)
LAW-362-01

4. MATLAB primer [2005]

Book
xii, 215 p. ; 17 cm.
  • ACCESSING MATLAB THE MATLAB DESKTOP Help Window Start Button Command Window Workspace Window Command History Window Array Editor Window Current Directory Window MATRICES AND MATRIX OPERATIONS Referencing Individual Entries Matrix Operators Matrix Division (Slash and Backslash) Entry-Wise Operators Relational Operators Complex Numbers Strings Other Data Types SUBMATRICES AND COLON NOTATION Generating Vectors Accessing Submatrices MATLAB FUNCTIONS Constructing Matrices Scalar Functions Vector Functions and Data Analysis Matrix Functions The linsolve Function The find Function CONTROL FLOW STATEMENTS The for Loop The while Loop The switch Statement The try/catch Statement Matrix Expressions (if and while) Infinite Loops M-FILES M-File Editor/Debugger Window Script Files Function Files Multiple Inputs and Outputs Variable Arguments Comments and Documentation MATLAB's Path ADVANCED M-FILE FEATURES Function Handles and Anonymous Functions Name Resolution Error and Warning Messages User Input Performance Measures Efficient Code CALLING C FROM MATLAB A Simple Example C Versus MATLAB Arrays A Matrix Computation in C MATLAB mx and mex Routines Online Help for MEX Routines Larger Examples on the Web CALLING FORTRAN FROM MATLAB Solving a Transposed System A Fortran mexFunction with %val If You Cannot Use %val CALLING JAVA FROM MATLAB A Simple Example Encryption/Decryption MATLAB's Java Class Path Calling Your Own Java Methods Loading a URL as a Matrix TWO-DIMENSIONAL GRAPHICS Planar Plots Multiple Figures Graph of a Function Parametrically Defined Curves Titles, Labels, and Text in a Graph Control of Axes and Scaling Multiple Plots Line Types, Marker Types, Colors Subplots and Specialized Plots Graphics Hard Copy THREE-DIMENSIONAL GRAPHS Curve Plots Mesh and Surface Plots Parametrically Defined Surfaces Volume and Vector Visualization Color Shading and Color Profile Perspective of View ADVANCED GRAPHICS Handle Graphics Graphical User Interface Images SPARSE MATRIX COMPUTATIONS Storage Modes Generating Sparse Matrices Computation with Sparse Matrices Ordering Methods Visualizing Matrices THE SYMBOLIC MATH TOOLBOX Symbolic Variables Calculus Variable Precision Arithmetic Numeric and Symbolic Substitution Algebraic Simplification Two-Dimensional Graphs Three Dimensional Surface Graphs Three-Dimensional Curves Symbolic Matrix Operations Symbolic Linear Algebraic Functions Solving Algebraic Equations Solving Differential Equations Further Maple Access POLYNOMIALS, INTERPOLATION, AND INTEGRATION Representing Polynomials Evaluating Polynomials Polynomial Interpolation Numeric Integration (Quadrature) SOLVING EQUATIONS Symbolic Equations Linear Systems of Equations Polynomial Roots Nonlinear Equations Ordinary Differential Equations Other Differential Equations DISPLAYING RESULTS CELL PUBLISHING CODE DEVELOPMENT TOOLS M-Lint Code Check Report TODO/FIXME Report Help Report Report Dependency Report File Comparison Report Profile and Coverage Report HELP TOPICS General Purpose Commands Operators and Special Characters Programming Language Constructs Elementary Matrices and Matrix Manipulation Elementary Math Functions Specialized Math Functions Matrix Functions-Numerical Linear Algebra Data Analysis, Fourier Transforms Interpolation and Polynomials Function Functions and ODEs Sparse Matrices Annotation and Plot Editing Two-Dimensional Graphs Three-Dimensional Graphs Specialized Graphs Handle Graphics Graphical User Interface Tools Character Strings Image and Scientific Data File Input/Output Audio and Video Support Time and Dates Data Types and Structures Version Control Creating and Debugging Code Help Commands Microsoft Windows Functions Examples and Demonstrations Preferences Symbolic Math Toolbox ADDITIONAL RESOURCES INDEX.
  • (source: Nielsen Book Data)9781584885238 20160528
With the spread of the powerhouse MATLAB[registered] software into nearly every area of math, science, and engineering, it is important to have a strong introduction to using the software. Updated for version 7.0, "MATLAB[registered] Primer, Seventh Edition" offers such an introduction as well as a 'pocketbook' reference for everyday users of the software. It offers an intuitive language for expressing problems and solutions both numerically and graphically. The latest edition in this best-selling series, "MATLAB[registered] Primer, Seventh Edition" incorporates a number of enhancements such as changes to the desktop, new features for developing M-files, the JIT accelerator, and an easier way of importing Java classes. In addition to the features new to version 7.0 , this book includes: a new section on M-Lint, the new debugger for M-files; a new chapter on calling Java from MATLAB and using Java objects inside the MATLAB workspace; a new chapter on calling Fortran from MATLAB; a new chapter on solving equations: symbolic and numeric polynomials, nonlinear equations, and differential equations; a new chapter on cell publishing, which replaces the 'notebook' feature and allows the creation of Word, LaTeX, PowerPoint, and HTML documents with executable MATLAB commands and their outputs; and, expanded Graphics coverage - including the 3D parametrically defined seashells on the front and back covers. Whether you are new to MATLAB, new to version 7.0, or simply in need of a hands-on, to-the-point reference, "MATLAB[registered] Primer" provides the tools you need in a conveniently sized, economically priced pocketbook.
(source: Nielsen Book Data)9781584885238 20160528
Law Library (Crown)
LAW-362-01
Book
xv, 351 p. : ill. ; 24 cm.
  • 1. Preliminaries. 2. Bayesian inference for the Normal Distribution. 3. Some other common distributions. 4. Hypothesis testing. 5. Two-sample problems. 6. Correlation, regression and ANOVA. 7. Other topics. 8. Hierarchical models. 9. The Gibbs sampler. Common statistical distributions. Tables. R programs. Further reading. References. Index.
  • (source: Nielsen Book Data)9780340814055 20160528
Bayesian Statistics is the school of thought that uses all information surrounding the likelihood of an event rather than just that collected experimentally. Among statisticians the Bayesian approach continues to gain adherents and this new edition of Peter Lee's well-established introduction maintains the clarity of exposition and use of examples for which this text is known and praised. In addition, there is extended coverage of the Metropolis-Hastings algorithm as well as an introduction to the use of BUGS (Bayesian Inference Using Gibbs Sampling) as this is now the standard computational tool for such numerical work. Other alterations include new material on generalized linear modelling and Bernardo's theory of reference points.
(source: Nielsen Book Data)9780340814055 20160528
Law Library (Crown)
LAW-362-01
Book
xiii, 704 p. : ill. ; 25 cm.
  • 1. Probability and Distribution. 2. Multivariate Distributions. 3. Some Special Distributions. 4. Unbiasedness, Consistency, and Limiting Distributions. 5. Introduction to Inference. 6. Maximum Likelihood Methods. 7. Sufficiency. 8. Optimal Tests of Hypotheses. 9. Inferences about Normal Models. 10. Nonparametric Statistics. 11. Bayesian Statistics. 12. Comparison of Least Squares and Robust Procedures for Linear Models. Appendix A. Regularity Conditions. Appendix B. R-Functions.
  • (source: Nielsen Book Data)9780130085078 20160528
For one or two-semester, undergraduate mathematical statistics course, or for beginning graduate courses in mathematical statistics. This classic text retains its outstanding features and continues to provide students with excellent background in the mathematics of statistics. Extensively revised with three new chapters.
(source: Nielsen Book Data)9780130085078 20160528
Law Library (Crown)
LAW-362-01
Book
xiii, 480 p. : ill. ; 25 cm.
  • The Bayesian method-- inference and decisions-- general principles and theory-- subjective probability-- non-subjective theories-- prior distributions-- model comparison-- robustness and model criticism-- computation-- Marcov Chain Monte Carlo-- the linear model-- generalized linear models-- nonparametric models-- other standard models-- short case studies.
  • (source: Nielsen Book Data)9780340807521 20160528
Kendall's objective in setting out to write the original Kendall's Advanced Theory of Statistics, published in two volumes in 1943 and 1946, was 'to develop a systematic treatment of [statistical theory] as it exists at the present time.' With this aim in mind, the first edition of Bayesian Inference was added as Volume 2B of the Kendall's Advanced Theory of Statistics in 1994, to introduce the new and rapidly growing field of Bayesian statistics. This new edition is a response to the developments and advances that have taken place in this area over the last few years and offers the reader an up-to-date and comprehensive overview of Bayesian statistics. The second edition of Bayesian Inference has been expanded to include new chapters on Markov Chain Monte Carlo methods, discrete data models and non-parametric models. Existing chapters have also been thoroughly revised and updated and there is greater coverage of computational methods and of model comparison and criticism. There is also a new chapter of case studies, providing practical illustrations of the theory presented throughout the book.Like the other volumes in the Kendall's Library of Statistics, the first edition of Bayesian Inference provided a good selection of exercises at the end of each chapter. This popular feature is retained in the new edition, with many new exercises to deepen the reader's understanding. Clearly written and offering a wide-ranging introduction to Bayesian statistics, this book will be an essential reference source for students, researchers and practitioners in statistics.
(source: Nielsen Book Data)9780340807521 20160528
Law Library (Crown)
LAW-362-01
Book
xii, 457 p. : ill. ; 25 cm.
  • Preface. The Basis for, and Advantages of, Bayesian Model Estimation via Repeated Sampling. Hierarchical Mixture Models. Regression Models. Analysis of Multi Level Data. Models for Time Series. Analysis of Panel Data. Models for Spatial Outcomes and Geographical Association. Structural Equation and Latent Variable Models. Survival and Event History Models. Modelling and Establishing Causal Relations: Epidemiological Methods and Models. Index.
  • (source: Nielsen Book Data)9780471486954 20160528
The use of Bayesian statistics has grown significantly in recent years, and will undoubtedly continue to do so. Applied Bayesian Modelling is the follow-up to the author's best selling book, Bayesian Statistical Modelling, and focuses on the potential applications of Bayesian techniques in a wide range of important topics in the social and health sciences. The applications are illustrated through many real-life examples and software implementation in WINBUGS - a popular software package that offers a simplified and flexible approach to statistical modelling. The book gives detailed explanations for each example - explaining fully the choice of model for each particular problem. The book Provides a broad and comprehensive account of applied Bayesian modelling. Describes a variety of model assessment methods and the flexibility of Bayesian prior specifications. Covers many application areas, including panel data models, structural equation and other multivariate structure models, spatial analysis, survival analysis and epidemiology. Provides detailed worked examples in WINBUGS to illustrate the practical application of the techniques described. All WINBUGS programs are available from an ftp site. The book provides a good introduction to Bayesian modelling and data analysis for a wide range of people involved in applied statistical analysis, including researchers and students from statistics, and the health and social sciences. The wealth of examples makes this book an ideal reference for anyone involved in statistical modelling and analysis.
(source: Nielsen Book Data)9780471486954 20160528
Law Library (Crown)
LAW-362-01
Book
xx, 459 p. : ill. ; 25 cm.
  • Background and Introduction. Likelihood Inference and the Generalized Linear Model. The Bayesian Setup. The Normal and Student's-T Models. The Bayesian Prior. Assessing Model Quality. Bayesian Hypothesis Testing and the Bayes Factor. Bayesian Posterior Simulation. Basics of Markov Chain Monte Carlo. Bayesian Hierarchical Models. Practical Markov Chain Monte Carlo.
  • (source: Nielsen Book Data)9781584882886 20160527
Despite increasing interest in Bayesian approaches, especially across the social sciences, it has been virtually impossible to find a text that introduces Bayesian data analysis in a manner accessible to social science students. The Bayesian paradigm is ideally suited to the type of data analysis they will have to perform, but the associated mathematics can be daunting. "Bayesian Methods: A Social and Behavioral Sciences Approach" presents the basic principles of Bayesian statistics in a treatment designed specifically for students in the social sciences and related fields.Requiring few prerequisites, it first introduces Bayesian statistics and inference with detailed descriptions of setting up a probability model, specifying prior distributions, calculating a posterior distribution, and describing the results. This is followed by explicit guidance on assessing model quality and model fit using various diagnostic techniques and empirical summaries. Finally, it introduces hierarchical models within the Bayesian context, which leads naturally to Markov Chain Monte Carlo computing techniques and other numerical methods. The author emphasizes practical computing issues, includes specific details for Bayesian model building and testing, and uses the freely available R and BUGS software for examples and exercise problems. The result is an eminently practical text that is comprehensive, rigorous, and ideally suited to teaching future empirical social scientists.
(source: Nielsen Book Data)9781584882886 20160527
Law Library (Crown)
LAW-362-01
Book
viii, 310 p. : ill. ; 25 cm.
At the beginning of the twentieth century, H. G. Wells predicted that statistical thinking would be as necessary for citizenship in a technological world as the ability to read and write. But in the twenty-first century, we are often overwhelmed by a baffling array of percentages and probabilities as we try to navigate in a world dominated by statistics. Cognitive scientist Gerd Gigerenzer says that because we haven't learned statistical thinking, we don't understand risk and uncertainty. In order to assess risk -- everything from the risk of an automobile accident to the certainty or uncertainty of some common medical screening tests -- we need a basic understanding of statistics. Astonishingly, doctors and lawyers don't understand risk any better than anyone else. Gigerenzer reports a study in which doctors were told the results of breast cancer screenings and then were asked to explain the risks of contracting breast cancer to a woman who received a positive result from a screening. The actual risk was small because the test gives many false positives. But nearly every physician in the study overstated the risk. Yet many people will have to make important health decisions based on such information and the interpretation of that information by their doctors. Gigerenzer explains that a major obstacle to our understanding of numbers is that we live with an illusion of certainty. Many of us believe that HIV tests, DNA fingerprinting, and the growing number of genetic tests are absolutely certain. But even DNA evidence can produce spurious matches. We cling to our illusion of certainty because the medical industry, insurance companies, investment advisers, and electioncampaigns have become purveyors of certainty, marketing it like a commodity. To avoid confusion, says Gigerenzer, we should rely on more understandable representations of risk, such as absolute risks. For example, it is said that a mammography screening reduces the risk of breast cancer by 25 percent. But in absolute risks, that means that out of every 1,000 women who do not participate in screening, 4 will die; while out of 1,000 women who do, 3 will die. A 25 percent risk reduction sounds much more significant than a benefit that 1 out of 1,000 women will reap. This eye-opening book explains how we can overcome our ignorance of numbers and better understand the risks we may be taking with our money, our health, and our lives.
(source: Nielsen Book Data)9780743205566 20160605
Law Library (Crown)
LAW-362-01
Book
xii, 180 p. : ill. ; 24 cm.
  • Introduction The Need for Analysis of Variance (ANOVA) Means, Variances, Sums of Squares and Degrees of Freedom Independent Group ANOVAs One-Factor Independent Groups ANOVA Multiple Comparisons: Independent Groups t-Tests Two-Factor Independent Groups ANOVA Repeated Measures ANOVAs One-Factor Repeated Measures ANOVA Multiple Comparisons: Dependent Measures t-Tests Two-Factor Mixed Measures ANOVA Two-Factor Repeated Measures ANOVA Overview and Final Thoughts Some Tips for Tests on ANOVA Every Day Benefits of a Feel for Statistics and for Evaluating Data.
  • (source: Nielsen Book Data)9780803970755 20160618
Organized so that the reader moves from the simplest type of design to more complex ones, the authors introduce five different kinds of ANOVA techniques and explain which design/analysis is appropriate to answer specific questions.
(source: Nielsen Book Data)9780803970755 20160618
Law Library (Crown)
LAW-362-01
Book
xvii, 419 p. : ill., maps ; 24 cm.
  • Approaches for Statistical Inference. The Bayes Approach. The Empirical Bayes Approach. Performance of Bayes Procedures. Bayesian Computation. Model Criticism and Selection. Special Methods and Models. Case Studies. Appendices.
  • (source: Nielsen Book Data)9781584881704 20160528
In recent years, Bayes and empirical Bayes (EB) methods have continued to increase in popularity and impact. Building on the first edition of their popular text, Carlin and Louis introduce these methods, demonstrate their usefulness in challenging applied settings, and show how they can be implemented using modern Markov chain Monte Carlo (MCMC) methods. Their presentation is accessible to those new to Bayes and empirical Bayes methods, while providing in-depth coverage valuable to seasoned practitioners.With its broad appeal as a text for those in biomedical science, education, social science, agriculture, and engineering, this second edition offers a relatively gentle and comprehensive introduction for students and practitioners already familiar with more traditional frequentist statistical methods. Focusing on practical tools for data analysis, the book shows how properly structured Bayes and EB procedures typically have good frequentist and Bayesian performance, both in theory and in practice.
(source: Nielsen Book Data)9781584881704 20160528
Law Library (Crown)
LAW-362-01
Book
xiv, 799 p. : ill. ; 26 cm.
  • Preface Difference Equations 2Lag Operators 3Stationary ARMA Processes 4Forecasting 5Maximum Likelihood Estimation 6Spectral Analysis 7Asymptotic Distribution Theory 8Linear Regression Models 9Linear Systems of Simultaneous Equations 10Covariance-Stationary Vector Processes 11Vector Autoregressions 12Bayesian Analysis 13The Kalman Filter 14Generalized Method of Moments 15Models of Nonstationary Time Series 16Processes with Deterministic Time Trends 17Univariate Processes with Unit Roots 18Unit Roots in Multivariate Time Series 19Cointegration 20Full-Information Maximum Likelihood Analysis of Cointegrated Systems 21Time Series Models of Heteroskedasticity 22Modeling Time Series with Changes in Regime A Mathematical Review B Statistical Tables C Answers to Selected Exercises D Greek Letters and Mathematical Symbols Used in the Text Author Index Subject Index.
  • (source: Nielsen Book Data)9780691042893 20160528
The last decade has brought dramatic changes in the way that researchers analyze economic and financial time series. This book synthesizes these recent advances and makes them accessible to first-year graduate students. James Hamilton provides the first adequate text-book treatments of important innovations such as vector autoregressions, generalized method of moments, the economic and statistical consequences of unit roots, time-varying variances, and nonlinear time series models. In addition, he presents basic tools for analyzing dynamic systems (including linear representations, autocovariance generating functions, spectral analysis, and the Kalman filter) in a way that integrates economic theory with the practical difficulties of analyzing and interpreting real-world data. "Time Series Analysis" fills an important need for a textbook that integrates economic theory, econometrics, and new results. The book is intended to provide students and researchers with a self-contained survey of time series analysis. It starts from first principles and should be readily accessible to any beginning graduate student, while it is also intended to serve as a reference book for researchers.
(source: Nielsen Book Data)9780691042893 20160528
Law Library (Crown)
LAW-362-01
Book
230 p. : ill. ; 24 cm.
Written in the same cartoon format as "The Cartoon History of the Universe" and "The Cartoon Guide to the Computer", this book provides a humorous tour through modern statistics as it is practiced in a wide variety of fields - from the humanities to the sciences. The book begins with a brief history of the subject, then proceeds to cover data analysis, probability and all the other topics crucial to the study of statistics. Other books by Larry Gonick include "The Cartoon Guide to Genetics", "The Cartoon Guide to the United States" and "The Cartoon Guide to Physics".
(source: Nielsen Book Data)9780062731029 20160528
Law Library (Crown)
LAW-362-01