Morris
H.DeGroot（1931-1989），世界著名的统计学家。生前曾任国际统计学会、美国科学促进会、统计学会、数理统计学会、计量经济学会会士。卡内基·梅隆大学教授，1957年加入该校，1966年创办该校统计系。DeGroot在学术上异常活跃和多产，曾发表一百多篇论文，还著有Optimal
StatisOcal Decisions和Statistics and the
Lawo为纪念他的著作对统计教学的贡献，国际贝叶斯分析学会特别设立了DeGroot奖表彰优秀统计学著作。
Mark
J.Schervish，世界著名的统计学家，美国统计学会、数理统计学会会士。于1979年获得伊利诺伊大学的博士学位，之后就在卡内基·梅隆大学统计系工作，教授数学、概率、统计和计算金融等课程，现为该系系主任。Schervish在学术上非常活跃，成果颇丰，还因在统计推断和贝叶斯统计方面的基石性工作而闻名，除本书外，他还著有Theory
ofStatistics和 Rethinking the Foundations of Statistics。
目錄：
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 sample
variance
8.4 the t distributio
8.5 confidence intervals
8.6 bayesian analysis of samples from a normal
distribution
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 normal
distributio
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
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