Note: Supplemental materials are not guaranteed with Rental or Used book purchases.
Purchase Benefits
What is included with this book?
Preface | p. xi |
Motivation and Basic Tools | p. 1 |
Introduction | p. 1 |
Illustrative Examples and Motivation | p. 2 |
Synthesis of Finite to Asymptotic Statistical Methods | p. 8 |
The Organization of the Book | p. 13 |
Basic Tools and Concepts | p. 15 |
Exercises | p. 38 |
Estimation Theory | p. 42 |
Introduction | p. 42 |
Basic Concepts | p. 42 |
Likelihood, Information, and Sufficiency | p. 45 |
Methods of Estimation | p. 55 |
Finite Sample Optimality Perspectives | p. 62 |
Concluding Notes | p. 65 |
Exercises | p. 66 |
Hypothesis Testing | p. 68 |
Introduction | p. 68 |
The Neyman-Pearson Paradigm | p. 68 |
Composite Hypotheses: Beyond the Neyman-Pearson Paradigm | p. 73 |
Invariant Tests | p. 80 |
Concluding Notes | p. 81 |
Exercises | p. 81 |
Elements of Statistical Decision Theory | p. 83 |
Introduction | p. 83 |
Basic Concepts | p. 83 |
Bayes Estimation Methods | p. 88 |
Bayes Hypothesis Testing | p. 93 |
Confidence Sets | p. 95 |
Concluding Notes | p. 97 |
Exercises | p. 97 |
Stochastic Processes: An Overview | p. 100 |
Introduction | p. 100 |
Processes with Markov Dependencies | p. 102 |
Discrete Time-Parameter Processes | p. 110 |
Continuous Time-Parameter Processes | p. 112 |
Exercises | p. 118 |
Stochastic Convergence and Probability Inequalities | p. 119 |
Introduction | p. 119 |
Modes of Stochastic Convergence | p. 121 |
Probability Inequalities and Laws of Large Numbers | p. 131 |
Extensions to Dependent Variables | p. 160 |
Miscellaneous Convergence Results | p. 164 |
Concluding Notes | p. 169 |
Exercises | p. 169 |
Asymptotic Distributions | p. 173 |
Introduction | p. 173 |
Some Important Tools | p. 177 |
Central Limit Theorems | p. 181 |
Rates of Convergence to Normality | p. 197 |
Projections and Variance-Stabilizing Transformations | p. 201 |
Quadratic Forms | p. 218 |
Order Statistics and Empirical Distributions | p. 221 |
Concluding Notes | p. 232 |
Exercises | p. 236 |
Asymptotic Behavior of Estimators and Tests | p. 240 |
Introduction | p. 240 |
Estimating Equations and Local Asymptotic Linearity | p. 240 |
Asymptotics for MLE | p. 245 |
Asymptotics for Other Classes of Estimators | p. 249 |
Asymptotic Efficiency of Estimators | p. 255 |
Asymptotic Behavior of Some Test Statistics | p. 259 |
Resampling Methods | p. 268 |
Concluding Remarks | p. 271 |
Exercises | p. 272 |
Categorical Data Models | p. 273 |
Introduction | p. 273 |
Nonparametric Goodness-of-Fit Tests | p. 275 |
Estimation and Goodness-of-Fit Tests: Parametric Case | p. 278 |
Some Other Important Statistics | p. 286 |
Concluding Notes | p. 288 |
Exercises | p. 289 |
Regression Models | p. 293 |
Introduction | p. 293 |
Generalized Least-Squares Procedures | p. 295 |
Robust Estimators | p. 308 |
Nonlinear Regression Models | p. 316 |
Generalized Linear Models | p. 317 |
Generalized Least-Squares Versus Generalized Estimating Equations | p. 328 |
Nonparametric Regression | p. 331 |
Concluding Notes | p. 335 |
Exercises | p. 336 |
Weak Convergence and Gaussian Processes | p. 338 |
Introduction | p. 338 |
Weak Invariance Principles | p. 338 |
Weak Convergence of Partial Sum Processes | p. 341 |
Weak Convergence of Empirical Processes | p. 350 |
Weak Convergence and Statistical Functionals | p. 360 |
Weak Convergence and Nonparametrics | p. 365 |
Strong Invariance Principles | p. 371 |
Concluding Notes | p. 372 |
Exercises | p. 373 |
Bibliography | p. 375 |
Index | p. 381 |
Table of Contents provided by Ingram. All Rights Reserved. |
The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.
The Used, Rental and eBook copies of this book are not guaranteed to include any supplemental materials. Typically, only the book itself is included. This is true even if the title states it includes any access cards, study guides, lab manuals, CDs, etc.