Details

Examples and Problems in Mathematical Statistics


Examples and Problems in Mathematical Statistics


Wiley Series in Probability and Statistics 1. Aufl.

von: Shelemyahu Zacks

106,99 €

Verlag: Wiley
Format: PDF
Veröffentl.: 11.12.2013
ISBN/EAN: 9781118606001
Sprache: englisch
Anzahl Seiten: 652

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Beschreibungen

Provides the necessary skills to solve problems in mathematical statistics through theory, concrete examples, and exercises With a clear and detailed approach to the fundamentals of statistical theory, Examples and Problems in Mathematical Statistics uniquely bridges the gap between theory andapplication and presents numerous problem-solving examples that illustrate the relatednotations and proven results. Written by an established authority in probability and mathematical statistics, each chapter begins with a theoretical presentation to introduce both the topic and the important results in an effort to aid in overall comprehension. Examples are then provided, followed by problems, and finally, solutions to some of the earlier problems. In addition, Examples and Problems in Mathematical Statistics features: Over 160 practical and interesting real-world examples from a variety of fields including engineering, mathematics, and statistics to help readers become proficient in theoretical problem solving More than 430 unique exercises with select solutions Key statistical inference topics, such as probability theory, statistical distributions, sufficient statistics, information in samples, testing statistical hypotheses, statistical estimation, confidence and tolerance intervals, large sample theory, and Bayesian analysis Recommended for graduate-level courses in probability and statistical inference, Examples and Problems in Mathematical Statistics is also an ideal reference for applied statisticians and researchers.
Preface xv List of Random Variables xvii List of Abbreviations xix 1 Basic Probability Theory 1 PART I: THEORY, 1 1.1 Operations on Sets, 1 1.2 Algebra and ?-Fields, 2 1.3 Probability Spaces, 4 1.4 Conditional Probabilities and Independence, 6 1.5 Random Variables and Their Distributions, 8 1.6 The Lebesgue and Stieltjes Integrals, 12 1.7 Joint Distributions, Conditional Distributions and Independence, 21 1.8 Moments and Related Functionals, 26 1.9 Modes of Convergence, 35 1.10 Weak Convergence, 39 1.11 Laws of Large Numbers, 41 1.12 Central Limit Theorem, 44 1.13 Miscellaneous Results, 47 PART II: EXAMPLES, 56 PART III: PROBLEMS, 73 PART IV: SOLUTIONS TO SELECTED PROBLEMS, 93 2 Statistical Distributions 106 PART I: THEORY, 106 2.1 Introductory Remarks, 106 2.2 Families of Discrete Distributions, 106 2.3 Some Families of Continuous Distributions, 109 2.4 Transformations, 118 2.5 Variances and Covariances of Sample Moments, 120 2.6 Discrete Multivariate Distributions, 122 2.7 Multinormal Distributions, 125 2.8 Distributions of Symmetric Quadratic Forms of Normal Variables, 130 2.9 Independence of Linear and Quadratic Forms of Normal Variables, 132 2.10 The Order Statistics, 133 2.11 t-Distributions, 135 2.12 F-Distributions, 138 2.13 The Distribution of the Sample Correlation, 142 2.14 Exponential Type Families, 144 2.15 Approximating the Distribution of the Sample Mean: Edgeworth and Saddlepoint Approximations, 146 PART II: EXAMPLES, 150 PART III: PROBLEMS, 167 PART IV: SOLUTIONS TO SELECTED PROBLEMS, 181 3 Sufficient Statistics and the Information in Samples 191 PART I: THEORY, 191 3.1 Introduction, 191 3.2 Definition and Characterization of Sufficient Statistics, 192 3.3 Likelihood Functions and Minimal Sufficient Statistics, 200 3.4 Sufficient Statistics and Exponential Type Families, 202 3.5 Sufficiency and Completeness, 203 3.6 Sufficiency and Ancillarity, 205 3.7 Information Functions and Sufficiency, 206 3.8 The Fisher Information Matrix, 212 3.9 Sensitivity to Changes in Parameters, 214 PART II: EXAMPLES, 216 PART III: PROBLEMS, 230 PART IV: SOLUTIONS TO SELECTED PROBLEMS, 236 4 Testing Statistical Hypotheses 246 PART I: THEORY, 246 4.1 The General Framework, 246 4.2 The Neyman–Pearson Fundamental Lemma, 248 4.3 Testing One-Sided Composite Hypotheses in MLR Models, 251 4.4 Testing Two-Sided Hypotheses in One-Parameter Exponential Families, 254 4.5 Testing Composite Hypotheses with Nuisance Parameters—Unbiased Tests, 256 4.6 Likelihood Ratio Tests, 260 4.7 The Analysis of Contingency Tables, 271 4.8 Sequential Testing of Hypotheses, 275 PART II: EXAMPLES, 283 PART III: PROBLEMS, 298 PART IV: SOLUTIONS TO SELECTED PROBLEMS, 307 5 Statistical Estimation 321 PART I: THEORY, 321 5.1 General Discussion, 321 5.2 Unbiased Estimators, 322 5.3 The Efficiency of Unbiased Estimators in Regular Cases, 328 5.4 Best Linear Unbiased and Least-Squares Estimators, 331 5.5 Stabilizing the LSE: Ridge Regressions, 335 5.6 Maximum Likelihood Estimators, 337 5.7 Equivariant Estimators, 341 5.8 Estimating Equations, 346 5.9 Pretest Estimators, 349 5.10 Robust Estimation of the Location and Scale Parameters of Symmetric Distributions, 349 PART II: EXAMPLES, 353 PART III: PROBLEMS, 381 PART IV: SOLUTIONS OF SELECTED PROBLEMS, 393 6 Confidence and Tolerance Intervals 406 PART I: THEORY, 406 6.1 General Introduction, 406 6.2 The Construction of Confidence Intervals, 407 6.3 Optimal Confidence Intervals, 408 6.4 Tolerance Intervals, 410 6.5 Distribution Free Confidence and Tolerance Intervals, 412 6.6 Simultaneous Confidence Intervals, 414 6.7 Two-Stage and Sequential Sampling for Fixed Width Confidence Intervals, 417 PART II: EXAMPLES, 421 PART III: PROBLEMS, 429 PART IV: SOLUTION TO SELECTED PROBLEMS, 433 7 Large Sample Theory for Estimation and Testing 439 PART I: THEORY, 439 7.1 Consistency of Estimators and Tests, 439 7.2 Consistency of the MLE, 440 7.3 Asymptotic Normality and Efficiency of Consistent Estimators, 442 7.4 Second-Order Efficiency of BAN Estimators, 444 7.5 Large Sample Confidence Intervals, 445 7.6 Edgeworth and Saddlepoint Approximations to the Distribution of the MLE: One-Parameter Canonical Exponential Families, 446 7.7 Large Sample Tests, 448 7.8 Pitman’s Asymptotic Efficiency of Tests, 449 7.9 Asymptotic Properties of Sample Quantiles, 451 PART II: EXAMPLES, 454 PART III: PROBLEMS, 475 PART IV: SOLUTION OF SELECTED PROBLEMS, 479 8 Bayesian Analysis in Testing and Estimation 485 PART I: THEORY, 485 8.1 The Bayesian Framework, 486 8.2 Bayesian Testing of Hypothesis, 491 8.3 Bayesian Credibility and Prediction Intervals, 501 8.4 Bayesian Estimation, 502 8.5 Approximation Methods, 506 8.6 Empirical Bayes Estimators, 513 PART II: EXAMPLES, 514 PART III: PROBLEMS, 549 PART IV: SOLUTIONS OF SELECTED PROBLEMS, 557 9 Advanced Topics in Estimation Theory 563 PART I: THEORY, 563 9.1 Minimax Estimators, 563 9.2 Minimum Risk Equivariant, Bayes Equivariant, and Structural Estimators, 565 9.3 The Admissibility of Estimators, 570 PART II: EXAMPLES, 585 PART III: PROBLEMS, 592 PART IV: SOLUTIONS OF SELECTED PROBLEMS, 596 References 601 Author Index 613 Subject Index 617
SHELEMYAHU ZACKS, PhD, is Distinguished Professor in the Department of Mathematical Sciences at Binghamton University. He has published several books and more than 170 journal articles on the design and analysis of experiments, statistical control of stochastic processes, statistical decision theory, sequential analysis, reliability, statistical methods in logistics, and sampling from finite populations. A Fellow of the American Statistical Association, Institute of Mathematical Sciences, and American Association for the Advancement of Sciences, Dr. Zacks is the author of Stage-Wise Adaptive Designs, also published by Wiley.
Provides the necessary skills to solve problems in mathematical statistics through theory, concrete examples, and exercises With a clear and detailed approach to the fundamentals of statistical theory, Examples and Problems in Mathematical Statistics uniquely bridges the gap between theory and application and presents numerous problem-solving examples that illustrate the related notations and proven results. Written by an established authority in probability and mathematical statistics, each chapter begins with a theoretical presentation to introduce both the topic and the important results in an effort to aid in overall comprehension. Examples are then provided, followed by problems, and finally, solutions to some of the earlier problems. In addition, Examples and Problems in Mathematical Statistics features: Over 160 practical and interesting real-world examples from a variety of fields including engineering, mathematics, and statistics to help readers become proficient in theoretical problem solving More than 430 unique exercises with select solutions Key statistical inference topics, such as probability theory, statistical distributions, sufficient statistics, information in samples, testing statistical hypotheses, statistical estimation, confidence and tolerance intervals, large sample theory, and Bayesian analysis Recommended for graduate-level courses in probability and statistical inference, Examples and Problems in Mathematical Statistics is also an ideal reference for applied statisticians and researchers.

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