A.W.范德瓦特著的《渐近统计(英文版)》是一部介绍渐近统计经典教材，内容实用而且数学理论论述严谨。书中除了介绍介绍渐近统计的核心内容——似然推断，M估计，渐近效率，U统计和秩过程，书中还涉及该领域的最新研究论题，如半参数模型，自助法，经验过程，等其他应用。本书各章有习题。

Preface
Notation
1. Introduction
1.1. Approximate Statistical Procedures
1.2. Asymptotic Optimality Theory
1.3. Limitations
1.4. The Index n
2. Stochastic Convergence
2.1. Basic Theory
2.2. Stochastic o and O Symbols
2.3. Characteristic Functions
2.4. Almost-Sure Representations
2.5. Convergence of Moments
2.6. Convergence-Determining Classes
2.7. Law of the Iterated Logarithm
2.8. Lindeberg-Feller Theorem
2.9. Convergence in Total Variation
Problems
3. Delta Method
3.1. Basic Result
3.2. Variance-Stabilizing Transformations
3.3. Higher-Order Expansions
3.4. Uniform Delta Method
3.5. Moments
Problems
4. Moment Estimators
4.1. Method of Moments
4.2. Exponential Families
Problems
5. M- and Z-Estimators
5.1. Introduction
5.2. Consistency
5.3. Asymptotic Normality
5.4. Estimated Parameters
5.5. Maximum Likelihood Estimators
5.6. Classical Conditions
5.7. One-Step Estimators
5.8. Rates of Convergence
5.9. Argmax Theorem
Problems
6. Contiguity
6.1. Likelihood Ratios
6.2. Contiguity
Problems
7. Local Asymptotic Normality
7.1. Introduction
7.2. Expanding the Likelihood
7.3. Convergence to a Normal Experiment
7.4. Maximum Likelihood
7.5. Limit Distributions under Alternatives
7.6. Local Asymptotic Normality
Problems
8. Efficiency of Estimators
8.1. Asymptotic Concentration
8,2, Relative Efficiency
8.3. Lower Bound for Experiments
8.4. Estimating Normal Means
8.5. Convolution Theorem
8.6. Almost-Everywhere Convolution Theorem
8.7. Local Asymptotic Minimax Theorem
8.8. Shrinkage Estimators
8,9. Achieving the Bound
8.10. Large Deviations
Problems
9. Limits of Experiments
9.1. Introduction
9.2. Asymptotic Representation Theorem
9.3. Asymptotic Normality
9.4. Uniform Distribution
9.5. Pareto Distribution
9.6. Asymptotic Mixed Normality
9.7. Heuristics
Problems
10. Bayes Procedures
10.1. Introduction
10.2. Bernstein-von Mises Theorem
10.3. Point Estimators
10.4. Consistency
Problems
11. Projections
11.1. Projections
11.2. Conditional Expectation
11.3. Projection onto Sums
11.4. Hoeffding Decomposition
Problems
12. U-Statistics
12.1. One-Sample U-Statistics
12.2. Two-Sample U-statistics
12.3. Degenerate U-Statistics
Problems
13. Rank, Sign, and Permutation Statistics
13.1. Rank Statistics
13.2. Signed Rank Statistics
13.3. Rank Statistics for Independence
13.4. Rank Statistics under Alternatives
13.5. Permutation Tests
13.6. Rank Central Limit Theorem
Problems
14. Relative Efficiency of Tests
14.1. Asymptotic Power Functions
14.2. Consistency
14.3. Asymptotic Relative Efficiency
14.4. Other Relative Efficiencies
14.5. Rescaling Rates
Problems
15. Efficiency of Tests
15.1. Asymptotic Representation Theorem
15.2. Testing Normal Means
15.3. Local Asymptotic Normality
15.4. One-Sample Location
15.5. Two-Sample Problems
Problems
16. Likelihood Ratio Tests
16.1. Introduction
16.2. Taylor Expansion
16.3. Using Local Asymptotic Normality
16.4. Asymptotic Power Functions
16.5. Bartlett Correction
16.6. Bahadur Efficiency
Problems
17. Chi-Square Tests
17.1. Quadratic Forms in Normal Vectors
17.2. Pearson Statistic
17.3. Estimated Parameters
17.4. Testing Independence
17.5. Goodness-of-Fit Tests
17.6. Asymptotic Efficiency
Problems
18. Stochastic Convergence in Metric Spaces
18.1. Metric and Normed Spaces
18.2. Basic Properties
18.3. Bounded Stochastic Processes
Problems
19. Empirical Processes
19.1. Empirical Distribution Functions
19.2. Empirical Distributions
19.3. Goodness-of-Fit Statistics
19.4. Random Functions
19.5. Changing Classes
19.6. Maximal Inequalities
Problems
20.