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数理统计(第2版)txt，chm，pdf，epub，mobi下载作者: 邵军 出版社: 世界图书出版公司 原作名: Mathematical Statistics(Second Edition) 出版年: 2009-10-1 页数: 591 定价: 65.00元 装帧: 平装 丛书: Springer Texts in Statistics 影印版 ISBN: 9787510005343 内容简介 · · · · · ·数理统计，ISBN：9787510005343，作者：（美）邵 著 目录 · · · · · ·Preface to the First EditionPreface to the Second Edition Chapter 1.Probability Theory 1.1 Probability Spaces and Random Elements 1.1.1σ－fields and measures 1.1.2 Measurable functions and distributions · · · · · · () Preface to the First Edition Preface to the Second Edition Chapter 1.Probability Theory 1.1 Probability Spaces and Random Elements 1.1.1σ－fields and measures 1.1.2 Measurable functions and distributions 1.2 Integration and Differentiation 1.2.1 Integration 1.2.2 Radon.Nikodym derivative 1.3 Distributions and Their Characteristics 1.3.1 Distributions and probability densities 1.3.2 Moments and moment inequalities 1.3.3 Moment generating and characteristic functions 1.4 Conditional Expectations 1.4.1 Conditional expectations 1.4.2 Independence 1.4.3 Conditional distributions 1.4.4 Markov chains and martingales 1.5 Asymptotic Theory 1.5.1 Convergence modes and stochastic orders 1.5.2 Weak convergence 1.5.3 Convergence of transformations 1.5.4 The law of large numbers 1.5.5 The central limit theorem 1.5.6 Edgeworth and Cornish-Fisher expansions 1.6 Exercises Chapter 2. Fundamentals of Statistics 2.1 Populations，Samples，and Models 2.1.1 Populations and samples 2.1.2 Parametric and nonparametric models 2.1.3 Exponential and location.scale families 2.2 Statistics.Sufficiency,and Completeness 2.2.1 Statistics and their distributions 2.2.2 Sufficiency and minimal sufficiency 2.2.3 Complete statistics 2.3 Statistical Decision Theory 2.3.1 Decision rules，lOSS functions，and risks 2.3.2 Admissibility and optimality 2.4 Statistical Inference 2.4.1 P0il）t estimators 2.4.2 Hypothesis tests 2.4.3 Confidence sets 2.5 Asymptotic Criteria and Inference 2.5.1 Consistency 2.5.2 Asymptotic bias，variance，and mse 2.5.3 Asymptotic inference 2.6 Exercises Chapter 3.Unbiased Estimation 3.1 The UMVUE 3.1.1 Sufficient and complete statistics 3.1.2 A necessary and.sufficient condition 3.1.3 Information inequality 3.1.4 Asymptotic properties of UMVUE's 3.2 U-Statistics 3.2.1 Some examples 3.2.2 Variances of U-statistics 3.2.3 The projection method 3.3 The LSE in Linear Models 3.3.1 The LSE and estimability 3.3.2 The UMVUE and BLUE 3.3.3 R0bustness of LSE's 3.3.4 Asymptotic properties of LSE's 3.4 Unbiased Estimators in Survey Problems 3.4.1 UMVUE's of population totals 3.4.2 Horvitz-Thompson estimators 3.5 Asymptotically Unbiased Estimators 3.5.1 Functions of unbiased estimators 3.5.2 The method ofmoments 3.5.3 V-statistics 3.5.4 The weighted LSE 3.6 Exercises Chapter 4.Estimation in Parametric Models 4.1 Bayes Decisions and Estimators 4.1.1 Bayes actions 4.1.2 Empirical and hierarchical Bayes methods 4.1.3 Bayes rules and estimators 4.1.4 Markov chain Mollte Carlo 4.2 Invariance...... 4.2.1 One-parameter location families 4.2.2 One-parameter seale families 4.2.3 General location-scale families 4.3 Minimaxity and Admissibility 4.3.1 Estimators with constant risks 4.3.2 Results in one-parameter exponential families 4.3.3 Simultaneous estimation and shrinkage estimators 4.4 The Method of Maximum Likelihood 4.4.1 The likelihood function and MLE's 4.4.2 MLE's in generalized linear models 4.4.3 Quasi-likelihoods and conditional likelihoods 4.5 Asymptotically Efficient Estimation 4.5.1 Asymptotic optimality 4.5.2 Asymptotic efficiency of MLE's and RLE's 4.5.3 Other asymptotically efficient estimators 4.6 Exercises Chapter 5.Estimation in Nonparametric Models 5.1 Distribution Estimators 5.1.1 Empirical C.d.f.'s in i.i.d.cases 5.1.2 Empirical likelihoods 5.1.3 Density estimation 5.1.4 Semi-parametric methods 5.2 Statistical Functionals 5.2.1 Differentiability and asymptotic normality 5.2.2 L-.M-.and R-estimators and rank statistics 5.3 Linear Functions of Order Statistics 5.3.1 Sample quantiles 5.3.2 R0bustness and efficiency 5.3.3 L-estimators in linear models 5.4 Generalized Estimating Equations 5.4.1 The GEE method and its relationship with others 5.4.2 Consistency of GEE estimators 5.4.3 Asymptotic normality of GEE estimators 5.5 Variance Estimation 5.5.1 The substitution.method 5.5.2 The jackknife 5.5.3 The bootstrap 5.6 Exercises Chapter 6.Hypothesis Tests 6.1 UMP Tests 6.1.1 The Neyman-Pearson lemma 6.1.2 Monotone likelihood ratio 6.1.3 UMP tests for two-sided hypotheses 6.2 UMP Unbiased Tests 6.2.1 Unbiasedness，similarity，and Neyman structure 6.2.2 UMPU tests in exponential families 6.2.3 UMPU tests in normal families …… Chapter 7 Confidence Sets References List of Notation List of Abbreviations Index of Definitions,Main Results,and Examples Author Index Subject Index · · · · · · () |
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