1118029852

9781118029855

9781118625750

1118625757

1118518950

9781118518953

9781118029855

132207822X

9781322078229

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.

Calculation of sampling distributions.

The Central Limit Theorem.

Maximum likelihood estimation and properties of estimators.

Confidence intervals and hypothesis tests.

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.

Cover; Half Title page; Title page; Copyright page; Preface; Acknowledgments; Chapter 1: Data and Case Studies; 1.1 Case Study: Flight Delays; 1.2 Case Study: Birth Weights of Babies; 1.3 Case Study: Verizon Repair Times; 1.4 Sampling; 1.5 Parameters and Statistics; 1.6 Case Study: General Social Survey; 1.7 Sample Surveys; 1.8 Case Study: Beer and Hot Wings; 1.9 Case Study: Black Spruce Seedlings; 1.10 Studies; 1.11 Exercises; Chapter 2: Exploratory Data Analysis; 2.1 Basic Plots; 2.2 Numeric Summaries; 2.3 Boxplots; 2.4 Quantiles and Normal Quantile Plots

2.5 Empirical Cumulative Distribution Functions2.6 Scatter Plots; 2.7 Skewness and Kurtosis; 2.8 Exercises; Chapter 3: Hypothesis Testing; 3.1 Introduction to Hypothesis Testing; 3.2 Hypotheses; 3.3 Permutation Tests; 3.4 Contingency Tables; 3.5 Chi-Square Test of Independence; 3.6 Test of Homogeneity; 3.7 Goodness-of-Fit: All Parameters Known; 3.8 Goodness-of-Fit: Some Parameters Estimated; 3.9 Exercises; Chapter 4: Sampling Distributions; 4.1 Sampling Distributions; 4.2 Calculating Sampling Distributions; 4.3 The Central Limit Theorem; 4.4 Exercises; Chapter 5: The Bootstrap

5.1 Introduction to the Bootstrap5.2 The Plug-in Principle; 5.3 Bootstrap Percentile Intervals; 5.4 Two Sample Bootstrap; 5.5 Other Statistics; 5.6 Bias; 5.7 Monte Carlo Sampling: The "Second Bootstrap Principle"; 5.8 Accuracy of Bootstrap Distributions; 5.9 How Many Bootstrap Samples are Needed?; 5.10 Exercises; Chapter 6: Estimation; 6.1 Maximum Likelihood Estimation; 6.2 Method of Moments; 6.3 Properties of Estimators; 6.4 Exercises; Chapter 7: Classical Inference: Confidence Intervals; 7.1 Confidence Intervals for Means; 7.2 Confidence Intervals in General

7.3 One-Sided Confidence Intervals7.4 Confidence Intervals for Proportions; 7.5 Bootstrap t Confidence Intervals; 7.6 Exercises; Chapter 8: Classical Inference: Hypothesis Testing; 8.1 Hypothesis Tests for Means and Proportions; 8.2 Type I and Type Ii Errors; 8.3 More on Testing; 8.4 Likelihood Ratio Tests; 8.5 Exercises; Chapter 9: Regression; 9.1 Covariance; 9.2 Correlation; 9.3 Least-Squares Regression; 9.4 The Simple Linear Model; 9.5 Resampling Correlation and Regression; 9.6 Logistic Regression; 9.7 Exercises; Chapter 10: Bayesian Methods; 10.1 Bayes'Theorem

10.2 Binomial Data, Discrete Prior Distributions10.3 Binomial Data, Continuous Prior Distributions; 10.4 Continuous Data; 10.5 Sequential Data; 10.6 Exercises; Chapter 11: Additional Topics; 11.1 Smoothed Bootstrap; 11.2 Parametric Bootstrap; 11.3 The Delta Method; 11.4 Stratified Sampling; 11.5 Computational Issues in Bayesian Analysis; 11.6 Monte Carlo Integration; 11.7 Importance Sampling; 11.8 Exercises; Appendix A: Review of Probability; A.1 Basic Probability; A.2 Mean and Variance; A.3 The Mean of A Sample of Random Variables; A.4 The Law of Averages; A.5 The Normal Distribution

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