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Mathematical statistics with resampling and R /
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 accessibl...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, N.J. :
J. Wiley & Sons,
©2011.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- 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