Models for Probability and Statistical Inference Theory and Applications

Name: Models for Probability and Statistical Inference Theory and Applications
Price: 143.02 USD
Availability: InStock
ISBN: 9780470073728

ISBN-10: 0470073721

ISBN-13: 9780470073728

Edition: 2008

Authors: James H. Stapleton

List price: $186.95

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Description:

This textbook is an introduction to probability and statistical inference for students. It contains a large amount of figures, with simulations and graphs, produced by the statistical package S-Plus(r), included throughout. It discusses methods for the computer simulation of observations from specified distributions and provides flexibility for instructors. Each section is followed by a range of problems, from simple to more complex with selected answers.

Book details

List price: $186.95
Copyright year: 2008
Publisher: John Wiley & Sons, Incorporated
Publication date: 12/17/2007
Binding: Hardcover
Pages: 464
Size: 6.40" wide x 9.39" long x 1.14" tall
Weight: 2.090
Language: English



Preface



Discrete Probability Models



Introduction



Sample Spaces, Events, and Probability Measures



Conditional Probability and Independence



Random Variables



Expectation



The Variance



Covariance and Correlation



Special Discrete Distributions



Introduction



The Binomial Distribution



The Hypergeometric Distribution



The Geometric and Negative Binomial Distributions



The Poisson Distribution



Continuous Random Variables



Introduction



Continuous Random Variables



Expected Values and Variances for Continuous Random Variables



Transformations of Random Variables



Joint Densities



Distributions of Functions of Continuous Random Variables



Special Continuous Distributions



Introduction



The Normal Distribution



The Gamma Distribution



Conditional Distributions



Introduction



Conditional Expectations for Discrete Random Variables



Conditional Densities and Expectations for Continuous Random Variables



Moment Generating Functions and Limit Theory



Introduction



Moment Generating Functions



Convergence in Probability and in Distribution and the Weak Law of Large Numbers



The Central Limit Theorem



Estimation



Introduction



Point Estimation



The Method of Moments



Maximum Likelihood



Consistency



The [delta]-Method



Confidence Intervals



Fisher Information, Cramer-Rao Bound and Asymptotic Normality of MLEs



Sufficiency



Testing of Hypotheses



Introduction



The Neyman-Pearson Lemma



The Likelihood Ratio Test



The p-Value and the Relationship between Tests of Hypotheses and Confidence Intervals



The Multivariate Normal, Chi-Square, t, and F Distributions



Introduction



The Multivariate Normal Distribution



The Central and Noncentral Chi-Square Distributions



Student's t-Distribution



The F-Distribution



Nonparametric Statistics



Introduction



The Wilcoxon Test and Estimator



One-Sample Methods



The Kolmogorov-Smirnov Tests



Linear Statistical Models



Introduction



The Principle of Least Squares



Linear Models



F-Tests for H[subscript 0]: [theta] = [Beta subscript 1] X[subscript 1] + ... + [Beta subscript k] X[subscript k][Epsilon] V[subscript 0], a Subspace of V



Two-Way Analysis of Variance



Frequency Data



Introduction



Confidence Intervals on Binomial and Poisson Parameters



Logistic Regression



Two-Way Frequency Tables



Chi-Square Goodness-of-Fit Tests



Miscellaneous Topics



Introduction



Survival Analysis



Bootstrapping



Bayesian Statistics



Sampling


References


Appendix


Answers to Selected Problems


Index