概率统计

charles j.stone，斯坦福大学统计学博士，现为加利福尼亚大学伯克利分校统计系教授，主要研究方向是非参数统计模型、统计软件。

本书是以作者在加利福尼亚大学伯克利分校统计学系给高年级本科生和研究生授课的教学讲义为基础写成的，前半部分为概率，后半部分为统计。书中的主要内容包括概率，随机变量及其分布、期望、连续及离散模型、独立性、条件概率分布、密度函数及期望、线性分析、线性回归、泊松分布、逻辑回归及泊松回归等。

chapter 1 random variables and their distributions 1 1.1 introduction 1 1.2 sample distributions 5 1.3 distributions 14 1.4 random variables 23 1.5 probability functions and density functions 33 1.6 distribution functions and quantiles 45 1.7 univariate transformations 60 1.8 independence 69 chapter 2 expectation 81 2.1 introduction 81 2.2 properties of expectation 91 2.3 variance 99 2.4 weak law of large numbers 110 2.5 simulation and the monte carlo method 121 chapter 3 special continuous models 134 3.1 gamma and beta distributions 134 3.2 the normal distribution 145 .3.3 normal approximation and the central limit theorem chapter 4 special discrete models 162 4.1 combinatorics ' 162 4.2 the binomial distribution 172 4.3 the multinomial distribution 188 4.4 the poisson distribution 195 4.5 the poisson process 204 chapter 5 dependence 209 5.1 covariance, linear prediction, and correlation 209 5.2 multivariate expectation 219 5.3 covariance and variance-covariance matrices 225 5.4 multiple linear prediction 236 5.5 multivariate density functions 242 5.6 invertible transformations 252 5.7 the multivariate normal distribution 263 chapter 6 conditioning 274 6.1 conditional distributions 274 6.2 sampling without replacement 285 6.3 hypergeometric distribution 292 6.4 conditional density functions 300 6.5 conditional expectation 307 6.6 prediction 316 6.7 conditioning and the multivariate normal distribution 322 6.8 random parameters 330 chapter 7 normal models 338 7.1 introduction 338 7.2 chi-square, t, and f distributions 344 7.3 confidence intervals 353 7.4 the t test of an inequality 365 7.5 the t test of an equality 375 7.6 the f test 388 chapter 8 introduction to linear regression 396 8.1 the method of least squares 396 8.2 factorial experiments 407 8.3 input-response and experimental models 415 chapter 9 linear analysis 427 9.1 linear spaces 427 9.2 identifiability 438 9.3 saturated spaces 447 9.4 inner products 454 9.5 orthogonal projections 470 9.6 normal equations 485 chapter 10 linear regression 494 10.1 least-squares estimation 494 10.2 sums of squares 506 10.3 distribution theory 515 10.4 sugar beet experiment 526 10.5 lube oil experiment 538 10.6 the t test 552 10.7 submodels 560 10.8 the f test 568 chapter 11 orthogonal arrays 579 11.1 main effects 579 11.2 interactions 595 11.3 experiments with factors having three levels' 611 11.4 randomization, blocking, and covariates 620 chapter 12 binomial and poisson models 635 12.1 nominal confidence intervals and tests 636 12.2 exact p-values 651 12.3 one-parameter exponential families 662 chapter 13 logistic regression and poisson regression 673 13.1 input-response and experimental models 675 13.2 maximum-likelihood estimation 686 13.3 existence and uniqueness of the maximum-likelihood estimate 699 13.4 iteratively reweighted least-squares method 709 13.5 normal approximation 723 13.6 the likelihood-ratio test 736 appendix a properties of vectors and matrices 751 appendix b summary of probability 760 b.1 random variables and their distributions 760 b.2 random vectors 769 appendix c summary of statistics 774 c.1 normal models 774 c.2 linear regression 779 c.3 binomial and poisson models 785 c.4 logistic regression and poisson regression 787 appendix d hints and answers 798 appendix e tables 828 index 833