Models for Probability and Statistical Inference: Theory and ApplicationsISBN: 9780470073728
464 pages
December 2007

Models for Probability and Statistical Inference was written over a fiveyear period and serves as a comprehensive treatment of the fundamentals of probability and statistical inference. With detailed theoretical coverage found throughout the book, readers acquire the fundamentals needed to advance to more specialized topics, such as sampling, linear models, design of experiments, statistical computing, survival analysis, and bootstrapping.
Ideal as a textbook for a twosemester sequence on probability and statistical inference, early chapters provide coverage on probability and include discussions of: discrete models and random variables; discrete distributions including binomial, hypergeometric, geometric, and Poisson; continuous, normal, gamma, and conditional distributions; and limit theory. Since limit theory is usually the most difficult topic for readers to master, the author thoroughly discusses modes of convergence of sequences of random variables, with special attention to convergence in distribution. The second half of the book addresses statistical inference, beginning with a discussion on point estimation and followed by coverage of consistency and confidence intervals. Further areas of exploration include: distributions defined in terms of the multivariate normal, chisquare, t, and F (central and noncentral); the one and twosample Wilcoxon test, together with methods of estimation based on both; linear models with a linear spaceprojection approach; and logistic regression.
Each section contains a set of problems ranging in difficulty from simple to more complex, and selected answers as well as proofs to almost all statements are provided. An abundant amount of figures in addition to helpful simulations and graphs produced by the statistical package SPlus(r) are included to help build the intuition of readers.
1.1 Discrete Probability Models.
1.2 Conditional Probability and Independence.
1.3 Random Variables.
1.4 Expectation.
1.5 The Variance.
1.6 Covariance and Correlation.
2. Special Discrete Distributions.
2.1 The Binomial Distribution.
2.2 The Hypergeometric Distribution.
2.3 The Geometric and Negative Binomial Distributions.
2.4 The Poisson Distribution.
3. Continuous Random Variables.
4.1 Continuous RV's and Their Distributions.
4.2 Expected Values and Variances.
4.3 Transformations of Random Variables.
4.4Joint Densities.
4 Special Continuous Distributions.
4.1 The Normal Distribution.
4.2 The Gamma Distribution.
5. Conditional Distributions.
5.1 The Discrete Case.
5.2 Conditional Expectations for the Discrete Case.
5.3 Conditional Densities and Expectations for Continuous RV's.
6. Limit Laws.
6.1 Moment Generating Functions.
6.2 Convergence in Probability and in Distribution.
6.3 The Central Limit Theorem.
6.4 The DeltaMethod.
7. Estimation.
7.1 Point Estimation.
7.2 The Method of Moments.
7.3 Maximum Likelihood.
7.4 Consistency.
7.5 The ΩMethod.
7.6 Confidence Intervals.
7.7 Fisher Information, The CramerRao Bound, and Asymptotic Normality of MLE's.
7.8 Sufficiency.
8. Testing Hypotheses.
8.1 Introduction.
8.2 The NeymanPearson Lemma.
8.3 The Likelihood Ratio Test.
8.4 The pValue and the Relationship Between Tests of Hypotheses and Confidence Intervals.
9. The Multivariate Normal, Chisquare, t, and FDistributions.
9.1 The Multivariate Normal Distribution.
9.2 The Central and Noncentral ChiSquare Distributions.
9.3 Student's tDistribution.
9.4 The FDistribution.
10.3 Nonparametric Statistics.
10.1 The Wilcoxon Test and Estimator.
10.2 One Sample Methods.
10.3 The KolmogorovSmirnov Tests.
11. Linear Models.
11.1 The Principle of Least Squares.
11.2 Linear Models.
11.3 FTests for H0.
11.4 TwoWay Analysis of Variance..
12. Frequency Data.
12.1 Logistic Regression.
12.2 TwoWay Frequency Tables.
12.3 ChiSquare Goodness of Fit Tests.
13. Miscellaneous Topics.
13.1 Survival Analysis.
13.2 Bootstrapping.
13.3 Bayesian Statistics.
13.4 Sampling.
James H. Stapleton, PhD, has recently retired after fortynine years as professor in the Department of Statistics and Probability at Michigan State University, including eight years as chairperson and almost twenty years as graduate director. Dr. Stapleton is the author of Linear Statistical Models (Wiley), and he received his PhD in mathematical statistics from Purdue University.

Exercises are included throughout the book and selected answers (not solutions) are also provided.

Each section is followed by a selection of problems, from simple to more complex. Almost all statements are backed up with proofs, with the exception of the continuity theorem for moment generating functions, and asymptotic theory for logistic and loglinear models.
 The Table of Contents provides flexibility for instructors who use the book for their courses. In the preface the author outlines possible sections to include and/or skip for instructors who have varied course objectives.
 Simulations, using SPlus® , are provided to show that the asymptotic theory provides good approximations. The freely available R software could also be used.

This book provides a large amount of figures, which typically are not found in mathematical statistics books.
"Highly recommended. Graduate students through professionals." (CHOICE, May 2008)
"The whole first part of the book is very readerfriendly and well written." (CHOICE May 2008)
Models for Probability and Statistical Inference: Theory and Applications (US $156.00)
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