概率统计（第四版 英文版）
作者：（美）德格鲁特，（美）舍维什 著
出版时间：2012年版
内容简介
这本举世公认的经典概率论与数理统计教材，几十年来畅销不衰，被很多名校采用，包括卡内基-梅隆大学、哈佛大学、麻省理工学院、华盛顿大学、芝加哥大学、康奈尔大学、杜克大学、加州大学洛杉矶分校等。《华章统计学原版精品系列：概率统计（英文版·第4版）》包括概率论、数理统计两部分，内容丰富完整，适当地选择某些章节，可以作为一学年的概率论与数理统计课程的教材，亦可作为一学期的概率论与随机过程的教材。适合数学、统计学、经济学等专业高年级本科生和研究生用，也可供统计工作人员用作参考书。
目录
1 introduction to probability
1.1 the history of probability
1.2 interpretatio of probability
1.3 experiments and events
1.4 set theory
1.5 the definition of probability
1.6 finite sample spaces
1.7 counting methods
1.8 combinatorial methods
1.9 multinomial coefficients
1.10 the probability of a union of events
1.11 statistical swindles
1.12 supplementary exercises
2 conditional probability
2.1 the definition of conditional probability
2.2 independent events
2.3 bayes’ theorem
2.4 the gambler’s ruin problem
2.5 supplementary exercises
3 random variables and distributio
3.1 random variables and discrete distributio
3.2 continuous distributio
3.3 the cumulative distribution function
3.4 bivariate distributio
3.5 marginal distributio
3.6 conditional distributio
3.7 multivariate distributio
3.8 functio of a random variable
3.9 functio of two or more random variables
3.10 markov chai
3.11 supplementary exercises
4 expectation
4.1 the expectation of a random variable
4.2 properties of expectatio
4.3 variance
4.4 moments
4.5 the mean and the median
4.6 covariance and correlation
4.7 conditional expectation
4.8 utility
4.9 supplementary exercises
5 special distributio
5.1 introduction
5.2 the bernoulli and binomial distributio
5.3 the hypergeometric distributio
5.4 the poisson distributio
5.5 the negative binomial distributio
5.6 the normal distributio
5.7 the gamma distributio
5.8 the beta distributio
5.9 the multinomial distributio
5.10 the bivariate normal distributio
5.11 supplementary exercises
6 large random samples
6.1 introduction
6.2 the law of large numbe
6.3 the central limit theorem
6.4 the correction for continuity
6.5 supplementary exercises
7 estimation
7.1 statistical inference
7.2 prior and posterior distributio
7.3 conjugate prior distributio
7.4 bayes estimato
7.5 maximum likelihood estimato
7.6 properties of maximum likelihood estimato
7.7 sufficient statistics
7.8 jointly sufficient statistics
7.9 improving an estimator
7.10 supplementary exercises
8 sampling distributio of estimato
8.1 the sampling distribution of a statistic
8.2 the chi-square distributio
8.3 joint distribution of the sample mean and samplevariance
8.4 the t distributio
8.5 confidence intervals
8.6 bayesian analysis of samples from a normaldistribution
8.7 unbiased estimato
8.8 fisher information
8.9 supplementary exercises
9 testing hypotheses
9.1 problems of testing hypotheses
9.2 testing simple hypotheses
9.3 uniformly most powerful tests
9.4 two-sided alternatives
9.5 the t test
9.6 comparing the mea of two normaldistributio
9.7 the f distributio
9.8 bayes test procedures
9.9 foundational issues
9.10 supplementary exercises
10 categorical data and nonparametric methods
10.1 tests of goodness-of-fit
10.2 goodness-of-fit for composite hypotheses
10.3 contingency tables
10.4 tests of homogeneity
10.5 simpson’s paradox
10.6 kolmogorov-smirnov tests
10.7 robust estimation
10.8 sign and rank tests
10.9 supplementary exercises
11 linear statistical models
11.1 the method of least squares
11.2 regression
11.3 statistical inference in simple linear regression
11.4 bayesian inference in simple linear regression
11.5 the general linear model and multiple regression
11.6 analysis of variance
11.7 the two-way layout
11.8 the two-way layout with replicatio
11.9 supplementary exercises
12 simulation
12.1 what is simulation?
12.2 why is simulation useful?
12.3 simulating specific distributio
12.4 importance sampling
12.5 markov chain monte carlo
12.6 the bootstrap
12.7 supplementary exercises
tables
a we to odd-numbered exercises
references
index
