数理统计基础Models for Probability and Statistical Inference-经管之家官网！

这次是清晰版，只有10M，而且还便宜！
对Poisson Gamma F分布等各种数理统计基础不熟的看过来！这本是比较好的基础书了
Models for Probability and Statistical Inference：Theory and Applications
by James H. Stapleton
pdf 470 pages
Content
1. Discrete Probability Models 1
1.1. Introduction, 1
1.2. Sample Spaces, Events, and Probability Measures, 2
1.3. Conditional Probability and Independence, 15
1.4. Random Variables, 27
1.5. Expectation, 37
1.6. The Variance, 47
1.7. Covariance and Correlation, 55
2. Special Discrete Distributions 62
2.1. Introduction, 62
2.2. The Binomial Distribution, 62
2.3. The Hypergeometric Distribution, 65
2.4. The Geometric and Negative Binomial Distributions, 68
2.5. The Poisson Distribution, 72
3. Continuous Random Variables 80
3.1. Introduction, 80
3.2. Continuous Random Variables, 80
3.3. Expected Values and Variances for Continuous Random Variables, 88
3.4. Transformations of Random Variables, 93
3.5. Joint Densities, 97
3.6. Distributions of Functions of Continuous Random Variables, 104
4. Special Continuous Distributions 110
4.1. Introduction, 110
4.2. The Normal Distribution, 111
4.3. The Gamma Distribution, 117
5. Conditional Distributions 125
5.1. Introduction, 125
5.2. Conditional Expectations for Discrete Random Variables, 130
5.3. Conditional Densities and Expectations for Continuous Random
Variables, 136
6. Moment Generating Functions and Limit Theory 145
6.1. Introduction, 145
6.2. Moment Generating Functions, 145
6.3. Convergence in Probability and in Distribution and the Weak
Law of Large Numbers, 148
6.4. The Central Limit Theorem, 155
7. Estimation 166
7.1. Introduction, 166
7.2. Point Estimation, 167
7.3. The Method of Moments, 171
7.4. Maximum Likelihood, 175
7.5. Consistency, 182
7.6. The -Method, 186
7.7. Confidence Intervals, 191
7.8. Fisher Information, Cram´er–Rao Bound and Asymptotic
Normality of MLEs, 201
7.9. Sufficiency, 207
8. Testing of Hypotheses 215
8.1. Introduction, 215
8.2. The Neyman–Pearson Lemma, 222
8.3. The Likelihood Ratio Test, 228
8.4. The p-Value and the Relationship between Tests of Hypotheses
and Confidence Intervals, 233
9. The Multivariate Normal, Chi-Square, t, and F Distributions 238
9.1. Introduction, 238
9.2. The Multivariate Normal Distribution, 238
9.3. The Central and Noncentral Chi-Square Distributions, 241
9.4. Student’s t-Distribution, 245
9.5. The F-Distribution, 254
10. Nonparametric Statistics 260
10.1. Introduction, 260
10.2. The Wilcoxon Test and Estimator, 262
10.3. One-Sample Methods, 271
10.4. The Kolmogorov–Smirnov Tests, 277
11. Linear Statistical Models 281
11.1. Introduction, 281
11.2. The Principle of Least Squares, 281
11.3. Linear Models, 290
11.4. F-Tests for H0:  = 1X1 + ··· + k Xk∈ V0, a Subspace of V, 299
11.5. Two-Way Analysis of Variance, 308
12. Frequency Data 319
12.1. Introduction, 319
12.2. Confidence Intervals on Binomial and Poisson Parameters, 319
12.3. Logistic Regression, 324
12.4. Two-Way Frequency Tables, 330
12.5. Chi-Square Goodness-of-Fit Tests, 340
13. Miscellaneous Topics 350
13.1. Introduction, 350
13.2. Survival Analysis, 350
13.3. Bootstrapping, 355
13.4. Bayesian Statistics, 362
13.5. Sampling, 369
References 378
Appendix 381
Answers to Selected Problems 411
Index 437