Mathematical Statistics with Resampling and R

Detalles Bibliográficos
Autor principal: Chihara, Laura M.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated, 2011.
Colección:New York Academy of Sciences Ser.
Temas:
Electronic books.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Intro
  • 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 Functions
  • 2.6 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 Bootstrap
  • 5.2 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 Intervals
  • 7.4 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 Distributions
  • 10.3 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

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