Models for Probability and Statistical Inference Theory and Applications

ISBN-10: 0470073721
ISBN-13: 9780470073728
Edition: 2008
List price: $166.00 Buy it from $87.76
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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  More...

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Book details

List price: $166.00
Copyright year: 2008
Publisher: John Wiley & Sons, Incorporated
Publication date: 12/17/2007
Binding: Hardcover
Pages: 464
Size: 6.50" wide x 9.50" long x 1.25" tall
Weight: 1.694
Language: English

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.

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

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