- Book
- xvii, 230 pages : illustrations ; 25 cm.
- 1. Introduction to Dynamic Models 1.1 Six Examples of Input/Output Dynamics 1.1.1 Smallpox in Montreal 1.1.2 Spread of Disease Equations 1.1.3 Filling a Container 1.1.4 Head Impact and Brain Acceleration 1.1.5 Compartment models and pharmacokinetics 1.1.6 Chinese handwriting 1.1.7 Where to go for More Dynamical Systems 1.2 What This Book Undertakes 1.3 Mathematical Requirements 1.4 Overview 2 DE notation and types 2.1 Introduction and Chapter Overview 2.2 Notation for Dynamical Systems 2.2.1 Dynamical System Variables 2.2.2 Dynamical System Parameters 2.2.3 Dynamical System Data Configurations 2.2.4 Mathematical Background 2.3 The Architecture of Dynamic Systems 2.4 Types of Differential Equations 2.4.1 Linear Differential Equations 2.4.2 Nonlinear Dynamical Systems 2.4.3 Partial Differential Equations 2.4.4 Algebraic and Other Equations 2.5 Data Configurations 2.5.1 Initial and Boundary Value Configurations 2.5.2 Distributed Data Configurations 2.5.3 Unobserved or Lightly Observed Variables 2.5.4 Observational Data and Measurement Models 2.6 Differential Equation Transformations 2.7 A Notation Glossary 3 Linear Differential Equations and Systems 3.1 Introduction and Chapter Overview 3.2 The First Order Stationary Linear Buffer 3.3 The Second Order Stationary Linear Equation 3.4 The mth Order Stationary Linear Buffer 3.5 Systems of Linear Stationary Equations 3.6 A Linear System Example: Feedback Control 3.7 Nonstationary Linear Equations and Systems 3.7.1 The First Order Nonstationary Linear Buffer 3.7.2 First Order Nonstationary Linear Systems 3.8 Linear Differential Equations Corresponding to Sets of Functions 3.9 Green's Functions for Forcing Function Inputs 4 Nonlinear Differential Equations 4.1 Introduction and Chapter Overview 4.2 The Soft Landing Modification 4.3 Existence and Uniqueness Results 4.4 Higher Order Equations 4.5 Input/Output Systems 4.6 Case Studies 4.6.1 Bounded Variation: The Catalytic Equation 4.6.2 Rate Forcing: The SIR Spread of Disease System 4.6.3 From Linear to Nonlinear: The FitzHugh-Nagumo Equations 4.6.4 Nonlinear Mutual Forcing: The Tank Reactor Equations 4.6.5 Modeling Nylon Production 5 Numerical Solutions 5.1 Introduction 5.2 Euler Methods 5.3 Runge-KuttaMethods 5.4 Collocation Methods 5.5 Numerical Problems 5.5.1 Stiffness 5.5.2 Discontinuous Inputs 5.5.3 Constraints and Transformations < 6 Qualitative Behavior 6.1 Introduction 6.2 Fixed Points 6.2.1 Stability 6.3 Global Analysis and Limit Cycles 6.3.1 Use of Conservation Laws 6.3.2 Bounding Boxes 6.4 Bifurcations 6.4.1 Transcritical Bifurcations 6.4.2 Saddle Node Bifurcations 6.4.3 Pitchfork Bifurcations 6.4.4 Hopf Bifurcations 6.5 Some Other Features 6.5.1 Chaos 6.5.2 Fast-Slow Systems 6.6 Non-autonomous Systems 6.7 Commentary 7 Trajectory Matching 7.1 Introduction 7.2 Gauss-Newton Minimization 7.2.1 Sensitivity Equations 7.2.2 Automatic Differentiation 7.3 Inference 7.4 Measurements on Multiple Variables 7.4.1 Multivariate Gauss-Newton Method 7.4.2 VariableWeighting using Error Variance 7.4.3 Estimating s2 7.4.4 Example: FitzHugh-NagumoModels 7.4.5 Practical Problems: Local Minima 7.4.6 Initial Parameter Values for the Chemostat Data 7.4.7 Identifiability 7.5 Bayesian Methods 7.6 Multiple Shooting and Collocation 7.7 Fitting Features 7.8 Applications: Head Impacts 8 Gradient Matching 8.1 Introduction 8.2 Smoothing Methods and Basis Expansions 8.3 Fitting the Derivative 8.3.1 Optimizing Integrated Squared Error (ISSE) 8.3.2 Gradient Matching for the Refinery Data 8.3.3 Gradient Matching and the Chemostat Data 8.4 System Mis-specification and Diagnostics 8.4.1 Diagnostic Plots 8.5 Conducting Inference 8.5.1 Nonparametric Smoothing Variances 8.5.2 Example: Refinery Data 8.6 Related Methods and Extensions 8.6.1 Alternative Smoothing Method 8.6.2 Numerical Discretization Methods 8.6.3 Unobserved Covariates 8.6.4 Nonparametric Models 8.6.5 Sparsity and High Dimensional ODEs 8.7 Integral Matching 8.8 Applications: Head Impacts 9 Profiling for Linear Systems 9.1 Introduction and Chapter Overview 9.2 Parameter Cascading 9.2.1 Two Classes of Parameters 9.2.2 Defining Coefficients as Functions of Parameters 9.2.3 Data/Equation Symmetry 9.2.4 Inner Optimization Criterion J 9.2.5 The Least Squares Cascade Coefficient Function 9.2.6 The Outer Fitting Criterion H 9.3 Choosing the Smoothing Parameter r 9.4 Confidence Intervals for Parameters 9.4.1 Simulation Sample Results 9.5 Multi-Variable Systems 9.6 Analysis of the Head Impact Data 9.7 A Feedback Model for Driving Speed 9.7.1 Two-Variable First Order Cruise Control Model 9.7.2 One-Variable Second Order Cruise Control Model 9.8 The Dynamics of the Canadian Temperature Data 9.9 Chinese Handwriting 9.10 Complexity Bases 9.11 Software and Computation 9.11.1 Rate Function Specifications 9.11.2 Model Term Specifications 9.11.3 Memoization 10 Nonlinear Profiling 10.1 Introduction and Chapter Overview 10.2 Parameter Cascading for Nonlinear Systems 10.2.1 The Setup for Parameter Cascading 10.2.2 Parameter Cascading Computations 10.2.3 Some Helpful Tips 10.2.4 Nonlinear Systems and Other Fitting Criteria 10.3 Lotka-Volterra 10.4 Head Impact 10.5 Compound Model for Blood Ethanol 10.6 Catalytic model for growth 10.7 Aromate Reactions References Glossary Index.
- (source: Nielsen Book Data)9781493971886 20171002
(source: Nielsen Book Data)9781493971886 20171002
- 1. Introduction to Dynamic Models 1.1 Six Examples of Input/Output Dynamics 1.1.1 Smallpox in Montreal 1.1.2 Spread of Disease Equations 1.1.3 Filling a Container 1.1.4 Head Impact and Brain Acceleration 1.1.5 Compartment models and pharmacokinetics 1.1.6 Chinese handwriting 1.1.7 Where to go for More Dynamical Systems 1.2 What This Book Undertakes 1.3 Mathematical Requirements 1.4 Overview 2 DE notation and types 2.1 Introduction and Chapter Overview 2.2 Notation for Dynamical Systems 2.2.1 Dynamical System Variables 2.2.2 Dynamical System Parameters 2.2.3 Dynamical System Data Configurations 2.2.4 Mathematical Background 2.3 The Architecture of Dynamic Systems 2.4 Types of Differential Equations 2.4.1 Linear Differential Equations 2.4.2 Nonlinear Dynamical Systems 2.4.3 Partial Differential Equations 2.4.4 Algebraic and Other Equations 2.5 Data Configurations 2.5.1 Initial and Boundary Value Configurations 2.5.2 Distributed Data Configurations 2.5.3 Unobserved or Lightly Observed Variables 2.5.4 Observational Data and Measurement Models 2.6 Differential Equation Transformations 2.7 A Notation Glossary 3 Linear Differential Equations and Systems 3.1 Introduction and Chapter Overview 3.2 The First Order Stationary Linear Buffer 3.3 The Second Order Stationary Linear Equation 3.4 The mth Order Stationary Linear Buffer 3.5 Systems of Linear Stationary Equations 3.6 A Linear System Example: Feedback Control 3.7 Nonstationary Linear Equations and Systems 3.7.1 The First Order Nonstationary Linear Buffer 3.7.2 First Order Nonstationary Linear Systems 3.8 Linear Differential Equations Corresponding to Sets of Functions 3.9 Green's Functions for Forcing Function Inputs 4 Nonlinear Differential Equations 4.1 Introduction and Chapter Overview 4.2 The Soft Landing Modification 4.3 Existence and Uniqueness Results 4.4 Higher Order Equations 4.5 Input/Output Systems 4.6 Case Studies 4.6.1 Bounded Variation: The Catalytic Equation 4.6.2 Rate Forcing: The SIR Spread of Disease System 4.6.3 From Linear to Nonlinear: The FitzHugh-Nagumo Equations 4.6.4 Nonlinear Mutual Forcing: The Tank Reactor Equations 4.6.5 Modeling Nylon Production 5 Numerical Solutions 5.1 Introduction 5.2 Euler Methods 5.3 Runge-KuttaMethods 5.4 Collocation Methods 5.5 Numerical Problems 5.5.1 Stiffness 5.5.2 Discontinuous Inputs 5.5.3 Constraints and Transformations < 6 Qualitative Behavior 6.1 Introduction 6.2 Fixed Points 6.2.1 Stability 6.3 Global Analysis and Limit Cycles 6.3.1 Use of Conservation Laws 6.3.2 Bounding Boxes 6.4 Bifurcations 6.4.1 Transcritical Bifurcations 6.4.2 Saddle Node Bifurcations 6.4.3 Pitchfork Bifurcations 6.4.4 Hopf Bifurcations 6.5 Some Other Features 6.5.1 Chaos 6.5.2 Fast-Slow Systems 6.6 Non-autonomous Systems 6.7 Commentary 7 Trajectory Matching 7.1 Introduction 7.2 Gauss-Newton Minimization 7.2.1 Sensitivity Equations 7.2.2 Automatic Differentiation 7.3 Inference 7.4 Measurements on Multiple Variables 7.4.1 Multivariate Gauss-Newton Method 7.4.2 VariableWeighting using Error Variance 7.4.3 Estimating s2 7.4.4 Example: FitzHugh-NagumoModels 7.4.5 Practical Problems: Local Minima 7.4.6 Initial Parameter Values for the Chemostat Data 7.4.7 Identifiability 7.5 Bayesian Methods 7.6 Multiple Shooting and Collocation 7.7 Fitting Features 7.8 Applications: Head Impacts 8 Gradient Matching 8.1 Introduction 8.2 Smoothing Methods and Basis Expansions 8.3 Fitting the Derivative 8.3.1 Optimizing Integrated Squared Error (ISSE) 8.3.2 Gradient Matching for the Refinery Data 8.3.3 Gradient Matching and the Chemostat Data 8.4 System Mis-specification and Diagnostics 8.4.1 Diagnostic Plots 8.5 Conducting Inference 8.5.1 Nonparametric Smoothing Variances 8.5.2 Example: Refinery Data 8.6 Related Methods and Extensions 8.6.1 Alternative Smoothing Method 8.6.2 Numerical Discretization Methods 8.6.3 Unobserved Covariates 8.6.4 Nonparametric Models 8.6.5 Sparsity and High Dimensional ODEs 8.7 Integral Matching 8.8 Applications: Head Impacts 9 Profiling for Linear Systems 9.1 Introduction and Chapter Overview 9.2 Parameter Cascading 9.2.1 Two Classes of Parameters 9.2.2 Defining Coefficients as Functions of Parameters 9.2.3 Data/Equation Symmetry 9.2.4 Inner Optimization Criterion J 9.2.5 The Least Squares Cascade Coefficient Function 9.2.6 The Outer Fitting Criterion H 9.3 Choosing the Smoothing Parameter r 9.4 Confidence Intervals for Parameters 9.4.1 Simulation Sample Results 9.5 Multi-Variable Systems 9.6 Analysis of the Head Impact Data 9.7 A Feedback Model for Driving Speed 9.7.1 Two-Variable First Order Cruise Control Model 9.7.2 One-Variable Second Order Cruise Control Model 9.8 The Dynamics of the Canadian Temperature Data 9.9 Chinese Handwriting 9.10 Complexity Bases 9.11 Software and Computation 9.11.1 Rate Function Specifications 9.11.2 Model Term Specifications 9.11.3 Memoization 10 Nonlinear Profiling 10.1 Introduction and Chapter Overview 10.2 Parameter Cascading for Nonlinear Systems 10.2.1 The Setup for Parameter Cascading 10.2.2 Parameter Cascading Computations 10.2.3 Some Helpful Tips 10.2.4 Nonlinear Systems and Other Fitting Criteria 10.3 Lotka-Volterra 10.4 Head Impact 10.5 Compound Model for Blood Ethanol 10.6 Catalytic model for growth 10.7 Aromate Reactions References Glossary Index.
- (source: Nielsen Book Data)9781493971886 20171002
(source: Nielsen Book Data)9781493971886 20171002
Science Library (Li and Ma)
Science Library (Li and Ma) | Status |
---|---|
Stacks | |
QA278 .R354 2017 | Unknown |
- Book
- 1 online resource.
EBSCOhost Access limited to 1 user
- EBSCOhost Access limited to 1 user
- Google Books (Full view)
- Book
- 1 online resource.
- Inference Framework and Method.- Measurement Error and Misclassification: Introduction.- Survival Data with Measurement Error.- Recurrent Event Data with Measurement Error.- Longitudinal Data with Covariate Measurement Error.- Multi-State Models with Error-Prone Data.- Case-Control Studies with Measurement Error or Misclassification.- Analysis with Error in Responses.- Miscellaneous Topics.- Appendix.- References.
- (source: Nielsen Book Data)9781493966387 20170925
(source: Nielsen Book Data)9781493966387 20170925
- Inference Framework and Method.- Measurement Error and Misclassification: Introduction.- Survival Data with Measurement Error.- Recurrent Event Data with Measurement Error.- Longitudinal Data with Covariate Measurement Error.- Multi-State Models with Error-Prone Data.- Case-Control Studies with Measurement Error or Misclassification.- Analysis with Error in Responses.- Miscellaneous Topics.- Appendix.- References.
- (source: Nielsen Book Data)9781493966387 20170925
(source: Nielsen Book Data)9781493966387 20170925
4. Multivariate analysis with LISREL [2016]
- Book
- 1 online resource.
- Preface
- Getting Started
- Regression Models
- Generalized Linear Models
- Multilevel Analysis
- Principal Components (PCA)
- Exploratory Factor Analysis (EFA)
- Confirmatory Factor Analysis(CFA)
- Structural Equation Models (SEM) with Latent Variables
- Analysis of Longitudinal Data
- Multiple Groups
- Appendix
- Basic Matrix Algebra and Statistics
- Testing Normality
- Computational Notes on Censored Regression
- Normal Scores
- Assessment of Fit
- General Statistical Theory
- Iteration Algorithms
- References. .
- Preface
- Getting Started
- Regression Models
- Generalized Linear Models
- Multilevel Analysis
- Principal Components (PCA)
- Exploratory Factor Analysis (EFA)
- Confirmatory Factor Analysis(CFA)
- Structural Equation Models (SEM) with Latent Variables
- Analysis of Longitudinal Data
- Multiple Groups
- Appendix
- Basic Matrix Algebra and Statistics
- Testing Normality
- Computational Notes on Censored Regression
- Normal Scores
- Assessment of Fit
- General Statistical Theory
- Iteration Algorithms
- References. .
- Book
- 1 online resource (xvii, 327 pages).
- Prior Processes.- Inference Based on Complete Data.- Inference Based on Incomplete Data.
- (source: Nielsen Book Data)9783319327884 20160912
(source: Nielsen Book Data)9783319327884 20160912
- Prior Processes.- Inference Based on Complete Data.- Inference Based on Incomplete Data.
- (source: Nielsen Book Data)9783319327884 20160912
(source: Nielsen Book Data)9783319327884 20160912
- Book
- 1 online resource.
- 1.Preliminaries.- 2. The Linear Hypothesis.- 3.Estimation.- 4.Hypothesis Testing.- 5.Inference Properties.- 6.Testing Several Hypotheses.- 7.Enlarging the Model.- 8.Nonlinear Regression Models.- 9.Multivariate Models.- 10.Large Sample Theory: Constraint-Equation Hypotheses.- 11.Large Sample Theory: Freedom-Equation Hypotheses.- 12.Multinomial Distribution.- Appendix.- Index.
- (source: Nielsen Book Data)9783319219295 20160619
(source: Nielsen Book Data)9783319219295 20160619
- 1.Preliminaries.- 2. The Linear Hypothesis.- 3.Estimation.- 4.Hypothesis Testing.- 5.Inference Properties.- 6.Testing Several Hypotheses.- 7.Enlarging the Model.- 8.Nonlinear Regression Models.- 9.Multivariate Models.- 10.Large Sample Theory: Constraint-Equation Hypotheses.- 11.Large Sample Theory: Freedom-Equation Hypotheses.- 12.Multinomial Distribution.- Appendix.- Index.
- (source: Nielsen Book Data)9783319219295 20160619
(source: Nielsen Book Data)9783319219295 20160619
9. Targeted learning [electronic resource] : causal inference for observational and experimental data [2011]
- Book
- xlix, 626 p.
dx.doi.org SpringerLink
- dx.doi.org SpringerLink
- Google Books (Full view)
- Book
- xviii, 353 p. : ill. ; 24 cm.
- One-Sample Problems.- Preliminaries (Building Blocks).- Graphical Tools.- Smooth Tests.- Methods Based on the Empirical Distribution Function.- Two-Sample and K-Sample Problems.- Preliminaries (Building Blocks).- Graphical Tools.- Some Important Two-Sample Tests.- Smooth Tests.- Methods Based on the Empirical Distribution Function.- Two Final Methods and Some Final Thoughts.
- (source: Nielsen Book Data)9780387927091 20160605
(source: Nielsen Book Data)9780387927091 20160605
- One-Sample Problems.- Preliminaries (Building Blocks).- Graphical Tools.- Smooth Tests.- Methods Based on the Empirical Distribution Function.- Two-Sample and K-Sample Problems.- Preliminaries (Building Blocks).- Graphical Tools.- Some Important Two-Sample Tests.- Smooth Tests.- Methods Based on the Empirical Distribution Function.- Two Final Methods and Some Final Thoughts.
- (source: Nielsen Book Data)9780387927091 20160605
(source: Nielsen Book Data)9780387927091 20160605
dx.doi.org SpringerLink
- dx.doi.org SpringerLink
- Google Books (Full view)
11. Design of observational studies [2010]
- Book
- xviii, 384 p. : ill. ; 25 cm.
- Introduction.- Matching to control bias from measured covariates.- Addressing bias from covariates that were not measured.
- (source: Nielsen Book Data)9781441912121 20160528
- Introduction.- Matching to control bias from measured covariates.- Addressing bias from covariates that were not measured.
- (source: Nielsen Book Data)9781441912121 20160528
dx.doi.org SpringerLink
- dx.doi.org SpringerLink
- Google Books (Full view)
Science Library (Li and Ma)
Science Library (Li and Ma) | Status |
---|---|
Stacks | |
QA279 .R668 2010 | Unknown |
13. Spatial statistics and modeling [2010]
- Book
- xv, 297 p. : ill., maps ; 25 cm.
- Second order spatial models and geostatistics.- Gibbs-Markov random fields on networks.- Spatial point processes.- Simulation of spatial models.- Statistics for spatial models.
- (source: Nielsen Book Data)9780387922560 20160528
- Second order spatial models and geostatistics.- Gibbs-Markov random fields on networks.- Spatial point processes.- Simulation of spatial models.- Statistics for spatial models.
- (source: Nielsen Book Data)9780387922560 20160528
dx.doi.org SpringerLink
- dx.doi.org SpringerLink
- Google Books (Full view)
Science Library (Li and Ma)
Science Library (Li and Ma) | Status |
---|---|
Stacks | |
QA278.2 .G34 2010 | Unknown |
- Book
- 1 online resource.
- Introduction.- Wigner matrices and semicircular law.- Sample covariance matrices and the Marcenko-Pastur law.- Product of two random matrices.- Limits of extreme eigenvalues.- Spectrum separation.- Semicircle law for Hadamard products.- Convergence rates of ESD.- CLT for linear spectral statistics.- Eigenvectors of sample covariance matrices.- Circular law.- Some applications of RMT.
- (source: Nielsen Book Data)9781441906601 20160528
- Introduction.- Wigner matrices and semicircular law.- Sample covariance matrices and the Marcenko-Pastur law.- Product of two random matrices.- Limits of extreme eigenvalues.- Spectrum separation.- Semicircle law for Hadamard products.- Convergence rates of ESD.- CLT for linear spectral statistics.- Eigenvectors of sample covariance matrices.- Circular law.- Some applications of RMT.
- (source: Nielsen Book Data)9781441906601 20160528
dx.doi.org SpringerLink
- dx.doi.org SpringerLink
- Google Books (Full view)
- Book
- 1 online resource (xviii, 353 p.) : ill.
- One-Sample Problems.- Preliminaries (Building Blocks).- Graphical Tools.- Smooth Tests.- Methods Based on the Empirical Distribution Function.- Two-Sample and K-Sample Problems.- Preliminaries (Building Blocks).- Graphical Tools.- Some Important Two-Sample Tests.- Smooth Tests.- Methods Based on the Empirical Distribution Function.- Two Final Methods and Some Final Thoughts.
- (source: Nielsen Book Data)9780387927091 20160605
(source: Nielsen Book Data)9780387927091 20160605
- One-Sample Problems.- Preliminaries (Building Blocks).- Graphical Tools.- Smooth Tests.- Methods Based on the Empirical Distribution Function.- Two-Sample and K-Sample Problems.- Preliminaries (Building Blocks).- Graphical Tools.- Some Important Two-Sample Tests.- Smooth Tests.- Methods Based on the Empirical Distribution Function.- Two Final Methods and Some Final Thoughts.
- (source: Nielsen Book Data)9780387927091 20160605
(source: Nielsen Book Data)9780387927091 20160605
- Book
- xxii, 745 p. : ill. ; 24 cm.
- Introduction.- Overview of supervised learning.- Linear methods for regression.- Linear methods for classification.- Basis expansions and regularization.- Kernel smoothing methods.- Model assessment and selection.- Model inference and averaging.- Additive models, trees, and related methods.- Boosting and additive trees.- Neural networks.- Support vector machines and flexible discriminants.- Prototype methods and nearest-neighbors.- Unsupervised learning.
- (source: Nielsen Book Data)9780387848570 20160619
(source: Nielsen Book Data)9780387848570 20160619
- Introduction.- Overview of supervised learning.- Linear methods for regression.- Linear methods for classification.- Basis expansions and regularization.- Kernel smoothing methods.- Model assessment and selection.- Model inference and averaging.- Additive models, trees, and related methods.- Boosting and additive trees.- Neural networks.- Support vector machines and flexible discriminants.- Prototype methods and nearest-neighbors.- Unsupervised learning.
- (source: Nielsen Book Data)9780387848570 20160619
(source: Nielsen Book Data)9780387848570 20160619
dx.doi.org SpringerLink
- dx.doi.org SpringerLink
- Google Books (Full view)
Marine Biology Library (Miller), Science Library (Li and Ma)
Marine Biology Library (Miller) | Status |
---|---|
Stacks | |
Q325.75 .H37 2009 | Unknown |
Science Library (Li and Ma) | Status |
---|---|
Stacks | |
Q325.75 .H37 2009 | Unknown On reserve at Li and Ma Science Library 2-hour loan |
Q325.75 .H37 2009 | Unknown On reserve at Li and Ma Science Library 2-hour loan |
Q325.75 .H37 2009 | Unknown On reserve at Li and Ma Science Library 2-hour loan |
STATS-315B-01, STATS-315B-01
- Course
- STATS-315B-01 -- Modern Applied Statistics: Data Mining
- Instructor(s)
- Friedman, Jerry H
- Course
- STATS-315B-01 -- Modern Applied Statistics: Data Mining
- Instructor(s)
- Hastie, Trevor John
17. Introduction to nonparametric estimation [2009]
- Book
- xii, 214 p. : ill. ; 25 cm.
- Nonparametric estimators.- Lower bounds on the minimax risk.- Asymptotic efficiency and adaptation.- Appendix.- References.- Index.
- (source: Nielsen Book Data)9780387790510 20160528
(source: Nielsen Book Data)9780387790510 20160528
This is a revised and extended version of the French book. The main changes are in Chapter 1 where the former Section 1. 3 is removed and the rest of the material is substantially revised. Sections 1. 2. 4, 1. 3, 1. 9, and 2. 7. 3 are new. Each chapter now has the bibliographic notes and contains the exercises section. I would like to thank Cristina Butucea, Alexander Goldenshluger, Stephan Huckenmann, Yuri Ingster, Iain Johnstone, Vladimir Koltchinskii, Alexander Korostelev, Oleg Lepski, Karim Lounici, Axel Munk, Boaz Nadler, AlexanderNazin, PhilippeRigollet, AngelikaRohde, andJonWellnerfortheir valuable remarks that helped to improve the text. I am grateful to Centre de Recherche en Economie et Statistique (CREST) and to Isaac Newton Ins- tute for Mathematical Sciences which provided an excellent environment for ?nishing the work on the book. My thanks also go to Vladimir Zaiats for his highly competent translation of the French original into English and to John Kimmel for being a very supportive and patient editor. Alexandre Tsybakov Paris, June 2008 Preface to the French Edition The tradition of considering the problem of statistical estimation as that of estimation of a ?nite number of parameters goes back to Fisher. However, parametric models provide only an approximation, often imprecise, of the - derlying statistical structure. Statistical models that explain the data in a more consistent way are often more complex: Unknown elements in these models are, in general, some functions having certain properties of smoo- ness.
(source: Nielsen Book Data)9781441927095 20160611
- Nonparametric estimators.- Lower bounds on the minimax risk.- Asymptotic efficiency and adaptation.- Appendix.- References.- Index.
- (source: Nielsen Book Data)9780387790510 20160528
(source: Nielsen Book Data)9780387790510 20160528
This is a revised and extended version of the French book. The main changes are in Chapter 1 where the former Section 1. 3 is removed and the rest of the material is substantially revised. Sections 1. 2. 4, 1. 3, 1. 9, and 2. 7. 3 are new. Each chapter now has the bibliographic notes and contains the exercises section. I would like to thank Cristina Butucea, Alexander Goldenshluger, Stephan Huckenmann, Yuri Ingster, Iain Johnstone, Vladimir Koltchinskii, Alexander Korostelev, Oleg Lepski, Karim Lounici, Axel Munk, Boaz Nadler, AlexanderNazin, PhilippeRigollet, AngelikaRohde, andJonWellnerfortheir valuable remarks that helped to improve the text. I am grateful to Centre de Recherche en Economie et Statistique (CREST) and to Isaac Newton Ins- tute for Mathematical Sciences which provided an excellent environment for ?nishing the work on the book. My thanks also go to Vladimir Zaiats for his highly competent translation of the French original into English and to John Kimmel for being a very supportive and patient editor. Alexandre Tsybakov Paris, June 2008 Preface to the French Edition The tradition of considering the problem of statistical estimation as that of estimation of a ?nite number of parameters goes back to Fisher. However, parametric models provide only an approximation, often imprecise, of the - derlying statistical structure. Statistical models that explain the data in a more consistent way are often more complex: Unknown elements in these models are, in general, some functions having certain properties of smoo- ness.
(source: Nielsen Book Data)9781441927095 20160611
Science Library (Li and Ma)
Science Library (Li and Ma) | Status |
---|---|
Stacks | |
QA278.8 .T7913 2009 | Unknown |
- Book
- 1 online resource (xii, 214 p.) : ill.
- Nonparametric estimators.- Lower bounds on the minimax risk.- Asymptotic efficiency and adaptation.- Appendix.- References.- Index.
- (source: Nielsen Book Data)9780387790510 20160612
(source: Nielsen Book Data)9780387790510 20160612
- Nonparametric estimators.- Lower bounds on the minimax risk.- Asymptotic efficiency and adaptation.- Appendix.- References.- Index.
- (source: Nielsen Book Data)9780387790510 20160612
(source: Nielsen Book Data)9780387790510 20160612
dx.doi.org SpringerLink
- dx.doi.org SpringerLink
- Google Books (Full view)
19. Monte Carlo and quasi-Monte Carlo sampling [2009]
- Book
- xvi, 373 p. : ill. ; 25 cm.
- The Monte Carlo method.- Sampling from known distributions.- Pseudorandom number generators.- Variance reduction techniques.- Quasi-Monte Carlo constructions.- Using quasi-Monte Carlo constructions.- Using quasi-Monte Carlo in practice.- Financial applications.- Beyond numerical integration.- Review of algebra.- Error and variance analysis for Halton sequences.- References.- Index.
- (source: Nielsen Book Data)9780387781648 20160528
(source: Nielsen Book Data)9780387781648 20160528
- The Monte Carlo method.- Sampling from known distributions.- Pseudorandom number generators.- Variance reduction techniques.- Quasi-Monte Carlo constructions.- Using quasi-Monte Carlo constructions.- Using quasi-Monte Carlo in practice.- Financial applications.- Beyond numerical integration.- Review of algebra.- Error and variance analysis for Halton sequences.- References.- Index.
- (source: Nielsen Book Data)9780387781648 20160528
(source: Nielsen Book Data)9780387781648 20160528
dx.doi.org SpringerLink
- dx.doi.org SpringerLink
- Google Books (Full view)
Science Library (Li and Ma)
Science Library (Li and Ma) | Status |
---|---|
Stacks | |
QA298 .L46 2009 | Unknown |
- Book
- xv, 781 p. : ill. ; 24 cm.
- Variability, information, prediction.- Kernel smoothing.- Spline smoothing.- New wave nonparametrics.- Supervised learning: Partition methods.- Alternative nonparametrics.- Computational comparisons.- Unsupervised learning: Clustering.- Learning in high dimensions.- Variable selection.- Multiple testing.
- (source: Nielsen Book Data)9780387981345 20160528
(source: Nielsen Book Data)9780387981345 20160528
- Variability, information, prediction.- Kernel smoothing.- Spline smoothing.- New wave nonparametrics.- Supervised learning: Partition methods.- Alternative nonparametrics.- Computational comparisons.- Unsupervised learning: Clustering.- Learning in high dimensions.- Variable selection.- Multiple testing.
- (source: Nielsen Book Data)9780387981345 20160528
(source: Nielsen Book Data)9780387981345 20160528
dx.doi.org SpringerLink
- dx.doi.org SpringerLink
- Google Books (Full view)
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