数理统计学导论（英文版·第7版）
出版时间：2012年版
内容简介
　　这本经典教材保持着一贯的风格，清晰地阐述基本理论，并且为了更好地让读者理解数理统计，还提供了一些重要的背景材料。内容覆盖估计和测试方面的古典统计推断方法，并深入介绍了充分性和测试理论，包括一致最佳检验和似然率。书中含有大量实例和练习，便于读者理解和巩固所学知识。
目录
Preface 1 Probability and Distributio 1．1 Introduction 1．2 Set Theory 1．3 The Probability Set Function 1．4 Conditional Probability and Independence 1．5 Random Variables 1．6 Discrete Random Variables 1．6．1 naformatio 1．7 Continuous Random Variables 1．7．1 naDSformatio 1．8 Expectation of a Random Variable 1．9 Some Special Expectatio 1．10 Important Inequalities2 Multivariate Distributio 2．1 Distributio of Two Random Variables 2．1．1Expectation 2．2 naformatio：Bivariate Random Variables 2．3 Conditional Distributio and Expectatio 2．4 The Correlation Coefficient 2．5 Independent Random Variables 2．6 Exteion to Several Random Variables 2．6．1*Multivariate Variance-Covariance Matrix 2．7 naformatio for Several Random Variables 2．8 Linear Combinatio of Random Variables3 Some Special Distributio 3．1 The Binomial and Related Distributio 3．2 The Poisson Distribution 3．3 The Г，χ2，andβ Distributio 3．4 The Normal Distribution 3．4．1Contaminated Normals 3．5 The Multivariate Normal Distribution 3．5．1*Applicatio 3．6 t-and F-Distributio 3．6．1 The t-distribution 3．6．2 The F-distribution 3．6．3 Student’S Theorem 3．7 Mixture Distributio 4 Some Elementary Statistical Inferences 4．1 Sampling and Statistics 4．1．1 Histogram Estimates of pmfs and pdfs 4．2 Confidence Intervals 4．2．1Confidence Intervals for Difference in Mea 4．2．2Confidence Interval for Difference in Proportio 4．3 Confidence Intervals for Paramete of Discrete Distributio 4．4 CIrder Statistics 4．4．1Quantiles 4．4．2Confidence Intervals for Quantiles 4．5 Introduction to Hypothesis Testing 4．6 Additional Comments About Statistical Tests 4．7 Chi-Square Tests 4．8 The Method of Monte Carlo 4．8．1 Accept-Reject Generation Algorithm 4．9 Bootstrap Procedures 4．9．1 Percentile Bootstrap Confidence Intervals 4．9．2Bootstrap Testing Procedures 4．10 *Tolerance Limits for Distributio5 Coistency and Limiting Distributio 5．1 Convergence in Probability 5．2 Convergence in Distribution 5．2．1Bounded in Probability 5．2．2 △-Method 5．2．3 Moment Generating Function Technique 5．3 Central Limit Theorem 5．4 *Exteio to Multivariate Distributio6 Maximum Likelihood Methods 6．1 Maximum Likeli．hood Estimation 6．2 Rao-Cram6r Lower Bound and E伍ciency 6．3 Maximum Likelihood Tests 6．4 Multiparameter Case：Estimation 6．5 Multiparameter Case：Testing 6．6 The EM Algorithm7 Sufficiency 7．1 Measures of Quality of Estimato 7．2 A Su伍cient Statistic for a Parameter 7．3 Properties of a Sufficient Statistic 7．4 Completeness and Uniqueness 7．5 The Exponential Class of Distributio 7．6 Functio of a Parameter 7．7 The Cuse of Several Paramete 7．8 Minimal Sufficiency and Ancillary Statistics 7．9 Sufficiency,Completeness．and Independence8 Optimal Tests of Hypotheses 8．1 Most Powerful Tests 8．2 Uniformly Most Powerful Tests 8．3 Likelihood Ratio Tests 8．4 The Sequential Probability Ratio Test 8．5Minimax and Classification Procedures 8．5．1 Minimax Procedures 8．5．2 Classification9 Inferences About Normal MOdels 9．1 Quadratic Forms 9．2 One-Way ANOVA 9．3 Noncentralχ2and F-Distributio 9．4 Multiple Compariso 9．5 The Analysis of Variance 9．6 A Regression Problem 9．7 A Test of Independence 9．8 The Distributio of Certain Quadratic Forms 9．9 The Independence of Certain Quadratic Forills10 Nonparametric and Robust Statistics 10．1 Location Models 10．2 Sample Median and the Sign Test 10．2．1 Asymptotic Relative Efficiency 10．2．2 Estimating Equatio Based on the Sign Test 10．2．3 Confidence Interval for the Median 10．3 Signed-Rank Wilcoxon 10．3．1 Asymptotic Relative Emciency 10．3．2 Estimating Equatio Based on Signed-Rank Wilcoxon 10．3．3 Confidence Interval for the Median 10．4 Mann-Whitnev-Wilcoxon Procedure 10．4．1 Asymptotic Relative Efficiency 10．4．2 Estimating Equatio Based on the Mann-Whitney-Wilcoxon 10．4．3 Confidence Interval for the Shift Parameter △ 10．5 General Rank Scores 10．5．1 Efficacy 10．5．2 Estimating Equatio Based on General Scores 10．5．3 0ptimization：Best Estimates 10．6 Adaptive Procedures 10．7 Simple Linear Model 10．8 Measures of Association 10．8．1 Kendall’S т 10．8．2 Spearman’S Rho 10．9 Robust Concepts 10．9．1 Location Model 10．9．2 Linear Model11 Bayesian Statistics 11．1 Subjective Probability 11．2 Bayesian Procedures 11．2．1 Prior and Posterior Distributio 11．2．2 Bayesian Point Estimation 11．2．3 Bayesian Interval Estimation 11．2．4 Bayesian Testing Procedures 11．2．5 Bayesian Sequential Procedures 11．3 More Bayesian Terminology and Ideas 11．4 Gibbs Sampler 11．5 Modern Bayesian Methods 11．5．1 Empirical BayesA Mathematical Comments A．1 Regularity Conditio A．2 SequencesB R FunctioC Tables of DistributioD Lists of Common DistributioE ReferencesF Awe to Selected Exercisds Index