Mathematical Statistics with Resampling and RThis book bridges the latest software applications with the benefits of modern resampling techniques Resampling helps students understand the meaning of sampling distributions, sampling variability, P-values, hypothesis tests, and confidence intervals. This groundbreaking book shows how to apply modern resampling techniques to mathematical statistics. Extensively class-tested to ensure an accessible presentation, Mathematical Statistics with Resampling and R utilizes the powerful and flexible computer language R to underscore the significance and benefits of modern resampling techniques. The book begins by introducing permutation tests and bootstrap methods, motivating classical inference methods. Striking a balance between theory, computing, and applications, the authors explore additional topics such as:
Throughout the book, case studies on diverse subjects such as flight delays, birth weights of babies, and telephone company repair times illustrate the relevance of the real-world applications of the discussed material. Key definitions and theorems of important probability distributions are collected at the end of the book, and a related website is also available, featuring additional material including data sets, R scripts, and helpful teaching hints. Mathematical Statistics with Resampling and R is an excellent book for courses on mathematical statistics at the upper-undergraduate and graduate levels. It also serves as a valuable reference for applied statisticians working in the areas of business, economics, biostatistics, and public health who utilize resampling methods in their everyday work. |
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Contents
Exploratory Data Analysis | 13 |
Estimation | 135 |
Confidence Intervals | 167 |
Hypothesis Testing | 211 |
Regression | 247 |
Bayesian Methods | 301 |
Additional Topics | 327 |
ImportanceSampling | 346 |
Appendix B Probability Distributions | 373 |
TheMultinomialDistribution | 374 |
TheGeometricDistribution | 376 |
TheNegativeBinomialDistribution | 377 |
TheHypergeometricDistribution | 378 |
ThePoissonDistribution | 379 |
TheUniformDistribution | 381 |
TheGammaDistribution | 382 |
Ratio Estimate for Importance Sampling | 352 |
Importance Sampling in Bayesian Applications | 355 |
Exercises | 359 |
Appendix A Review of Probability | 363 |
MeanandVariance | 364 |
The Mean of a Sample of Random Variables | 366 |
TheLawofAverages | 367 |
TheNormalDistribution | 368 |
SumsofNormalRandomVariables | 369 |
Higher Moments and the Moment Generating Function | 370 |
Other editions - View all
Mathematical Statistics with Resampling and R Laura M. Chihara,Tim C. Hesterberg Limited preview - 2012 |