Pt. 1. Basic Probability. 1. Modes of Convergence. 2. Partial Converses to Theorem 1. 3. Convergence in Law. 4. Laws of Large Numbers. 5. Central Limit Theorems -- Pt. 2. Basic Statistical Large Sample Theory. 6. Slutsky Theorems. 7. Functions of the Sample Moments. 8. The Sample Correlation Coefficient. 9. Pearson's Chi-Square. 10. Asymptotic Power of the Pearson Chi-Square Test -- Pt. 3. Special Topics. 11. Stationary m-Dependent Sequences. 12. Some Rank Statistics. 13. Asymptotic Distribution of Sample Quantiles. 14. Asymptotic Theory of Extreme Order Statistics. 15. Asymptotic Joint Distributions of Extrema -- Pt. 4. Efficient Estimation and Testing. 16. A Uniform Strong Law of Large Numbers. 17. Strong Consistency of Maximum-Likelihood Estimates. 18. Asymptotic Normality of the Maximum-Likelihood Estimate. 19. The Cramer-Rao Lower Bound. 20. Asymptotic Efficiency. 21. Asymptotic Normality of Posterior Distributions. 22. Asymptotic Distribution of the Likelihood Ratio Test Statistic. 23. Minimum Chi-Square Estimates. 24. General Chi-Square Tests
Analysis
Statistics
Statistics
Notes
Bibliography: p236-237. - Includes index
Bibliography
Includes bibliographical references (pages 236-239) and index
