Читать книгу Handbook of Regression Analysis With Applications in R - Samprit Chatterjee - Страница 2

1  Cover

2  Preface to the Second Edition

3  Preface to the First Edition How to Use This Book

4  PART ONE: The Multiple Linear Regression Model CHAPTER ONE: Multiple Linear Regression 1.1 Introduction 1.2 Concepts and Background Material 1.3 Methodology 1.4 Example — Estimating Home Prices 1.5 Summary CHAPTER TWO: Model Building 2.1 Introduction 2.2 Concepts and Background Material 2.3 Methodology 2.4 Indicator Variables and Modeling Interactions 2.5 Summary

5  PART TWO: Addressing Violations of Assumptions CHAPTER THREE: Diagnostics for Unusual Observations 3.1 Introduction 3.2 Concepts and Background Material 3.3 Methodology 3.4 Example — Estimating Home Prices (continued) 3.5 Summary CHAPTER FOUR: Transformations and Linearizable Models 4.1 Introduction 4.2 Concepts and Background Material: The Log‐Log Model 4.3 Concepts and Background Material: Semilog Models 4.4 Example — Predicting Movie Grosses After One Week 4.5 Summary CHAPTER FIVE: Time Series Data and Autocorrelation 5.1 Introduction 5.2 Concepts and Background Material 5.3 Methodology: Identifying Autocorrelation 5.4 Methodology: Addressing Autocorrelation 5.5 Summary

6  PART THREE: Categorical Predictors CHAPTER SIX: Analysis of Variance 6.1 Introduction 6.2 Concepts and Background Material 6.3 Methodology 6.4 Example — DVD Sales of Movies 6.5 Higher‐Way ANOVA 6.6 Summary CHAPTER SEVEN: Analysis of Covariance 7.1 Introduction 7.2 Methodology 7.3 Example — International Grosses of Movies 7.4 Summary

7  PART FOUR: Non‐Gaussian Regression Models CHAPTER EIGHT: Logistic Regression 8.1 Introduction 8.2 Concepts and Background Material 8.3 Methodology 8.4 Example — Smoking and Mortality 8.5 Example — Modeling Bankruptcy 8.6 Summary CHAPTER NINE: Multinomial Regression 9.1 Introduction 9.2 Concepts and Background Material 9.3 Methodology 9.4 Example — City Bond Ratings 9.5 Summary CHAPTER TEN: Count Regression 10.1 Introduction 10.2 Concepts and Background Material 10.3 Methodology 10.4 Overdispersion and Negative Binomial Regression 10.5 Example — Unprovoked Shark Attacks in Florida 10.6 Other Count Regression Models 10.7 Poisson Regression and Weighted Least Squares 10.8 Summary CHAPTER ELEVEN: Models for Time‐to‐Event (Survival) Data 11.1 Introduction 11.2 Concepts and Background Material 11.3 Methodology 11.4 Example — The Survival of Broadway Shows (continued) 11.5 Left‐Truncated/Right‐Censored Data and Time‐Varying Covariates 11.6 Summary

8  PART FIVE: Other Regression Models CHAPTER TWELVE: Nonlinear Regression 12.1 Introduction 12.2 Concepts and Background Material 12.3 Methodology 12.4 Example — Michaelis‐Menten Enzyme Kinetics 12.5 Summary CHAPTER THIRTEEN: Models for Longitudinal and Nested Data 13.1 Introduction 13.2 Concepts and Background Material 13.3 Methodology 13.4 Example — Tumor Growth in a Cancer Study 13.5 Example — Unprovoked Shark Attacks in the United States 13.6 Summary CHAPTER FOURTEEN: Regularization Methods and Sparse Models 14.1 Introduction 14.2 Concepts and Background Material 14.3 Methodology 14.4 Example — Human Development Index 14.5 Summary

9  PART SIX: Nonparametric and Semiparametric Models CHAPTER FIFTEEN: Smoothing and Additive Models 15.1 Introduction 15.2 Concepts and Background Material 15.3 Methodology 15.4 Example — Prices of German Used Automobiles 15.5 Local and Penalized Likelihood Regression 15.6 Using Smoothing to Identify Interactions 15.7 Summary CHAPTER SIXTEEN: Tree‐Based Models 16.1 Introduction 16.2 Concepts and Background Material 16.3 Methodology 16.4 Examples 16.5 Trees for Other Types of Data 16.6 Summary

10  Bibliography

11  Index

12  End User License Agreement