本书在讲述一阶大样本理论方面比较独特，讨论了大量的应用，包括密度估计、自助法和抽样方法论的渐进。本书的内容比较基础，适合统计专业的研究生和有两年微积分背景的应用领域。每章末有针对本章每节的问题和练习，每节末都附有小结。

Preface

1 Mathematical Background

 1.1 The concept of limit

 1.2 Embedding sequences

 1.3 Infinite series

 1.4 Order relations and rates of convergence

 1.5 Continuity

 1.6 Distributions

 1.7 Problems

2 Convergence in Probability and in Law

 2.1 Convergence in probability

 2.2 Applications

 2.3 Convergence in law

 2.4 The central limit theorem

 2.5 Taylor's theorem and the delta method

 2.6 Uniform convergence

 2.7 The CLT for independent non-identical random variables

 2.8 Central limit theorem for dependent variables

 2.9 Problems

3 Performance of Statistical Tests

 3.1 Critical values

 3.2 Comparing two treatments

 3.3 Power and sample size

 3.4 Comparison of tests: Relative efficiency

 3.5 Robustness

 3.6 Problems

4 Estimation

 4.1 Confidence intervals

 4.2 Accuracy of point estimators

 4.3 Comparing estimators

 4.4 Sampling from a finite population

 4.5 Problems

5 Multivariate Extensions

 5.1 Convergence of multivariate distributions

 5.2 The bivariate normal distribution

 5.3 Some linear algebra

 5.4 The multivariate normal distribution

 5.5 Some applications

 5.6 Estimation and testing in 2 × 2 tables

 5.7 Testing goodness of fit

 5.8 Problems

6 Nonparametric Estimation

 6.1 U-Statistics

 6.2 Statistical functionals

 6.3 Limit distributions of statistical functionals

 6.4 Density estimation

 6.5 Bootstrapping

 6.6 Problems

7 Efficient Estimators and Tests

 7.1 Maximum likelihood

 7.2 Fisher information

 7.3 Asymptotic normality and multiple roots

 7.4 Efficiency

 7.5 The multiparameter case I. Asymptotic normality

 7.6 The multiparameter case II. Efficiency

 7.7 Tests and confidence intervals

 7.8 Contingency tables

 7.9 Problems

Appendix

References

Author Index

Subject Index