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Preface | |
Basic Probability | p. 1 |
Modes of Convergence | p. 3 |
Partial Converses to Theorem 1 | p. 8 |
Convergence in Law | p. 13 |
Laws of Large Numbers | p. 19 |
Central Limit Theorems | p. 26 |
Basic Statistical Large Sample Theory | p. 37 |
Slutsky Theorems | p. 39 |
Functions of the Sample Moments | p. 44 |
The Sample Correlation Coefficient | p. 51 |
Pearson's Chi-Square | p. 56 |
Asymptotic Power of the Pearson Chi-Square Test | p. 61 |
Special Topics | p. 67 |
Stationary m-Dependent Sequences | p. 69 |
Some Rank Statistics | p. 75 |
Asymptotic Distribution of Sample Quantiles | p. 87 |
Asymptotic Theory of Extreme Order Statistics | p. 94 |
Asymptotic Joint Distributions of Extrema | p. 101 |
Efficient Estimation and Testing | p. 105 |
A Uniform Strong Law of Large Numbers | p. 107 |
Strong Consistency of Maximum-Likelihood Estimates | p. 112 |
Asymptotic Normality of the Maximum-Likelihood Estimate | p. 119 |
The Cramer-Rao Lower Bound | p. 126 |
Asymptotic Efficiency | p. 133 |
Asymptotic Normality of Posterior Distributions | p. 140 |
Asymptotic Distribution of the Likelihood Ratio Test Statistic | p. 144 |
Minimum Chi-Square Estimates | p. 151 |
General Chi-Square Tests | p. 153 |
Appendix: Solutions to the exercises | p. 172 |
References | p. 236 |
Index | p. 239 |
Table of Contents provided by Blackwell. All Rights Reserved. |
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