Handbook of regression analysis with applications in R / Samprit Chatterjee, New York University, Jeffrey S. Simonoff, New York University.

Format:

Book

Author/Creator:

Chatterjee, Samprit, 1938- author.

Simonoff, Jeffrey S., author.

Series:

Wiley series in probability and statistics

Standardized Title:

Handbook of regression analysis

Language:

English

Subjects (All):

Regression analysis--Handbooks, manuals, etc.

Regression analysis.

R (Computer program language).

Genre:

Handbooks and manuals.

Physical Description:

xxii, 349 pages : illustrations ; 24 cm.

Edition:

Second edition.

Place of Publication:

Hoboken, NJ : John Wiley & Sons, Inc., [2020]

Summary:

"Building on the Handbook of Regression Analysis and Regression Analysis by Example, the authors' thorough treatments of "classic" regression analysis, this book covers two important and more advanced topics of time-to-event survival data and longitudinal and clustered data. Further, methods that have become prominent in the last 15-30 years that are designed for analyses on often-large data sets and can take advantage of exibility in modeling were not covered, including smoothing, tree- based, and regularization methods, all of which are increasingly becoming part of the data analysis toolkit. Examples are drawn from a wide variety of application areas using real data sets and all of the R code is provided. The book will be of interest to data scientists as well as in regression analysis courses at the graduate and undergraduate level. Regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. More specifically, regression analysis helps one understand how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed. Most commonly, regression analysis estimates the conditional expectation of the dependent variable given the independent variables -- that is, the average value of the dependent variable when the independent variables are fixed. Regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning"-- Provided by publisher.

Contents:

Machine generated contents note: pt. I THE MULTIPLE LINEAR REGRESSION MODEL

1. Multiple Linear Regression

1.1. Introduction

1.2. Concepts and Background Material

1.2.1. The Linear Regression Model

1.2.2. Estimation Using Least Squares

1.2.3. Assumptions

1.3. Methodology

1.3.1. Interpreting Regression Coefficients

1.3.2. Measuring the Strength of the Regression Relationship

1.3.3. Hypothesis Tests and Confidence Intervals for β

1.3.4. Fitted Values and Predictions

1.3.5. Checking Assumptions Using Residual Plots

1.4. Example

- Estimating Home Prices

1.5. Summary

2. Model Building

2.1. Introduction

2.2. Concepts and Background Material

2.2.1. Using Hypothesis Tests to Compare Models

2.2.2. Collinearity

2.3. Methodology

2.3.1. Model Selection

2.3.2. Example

2.4. Indicator Variables and Modeling Interactions

2.4.1. Example

- Electronic Voting and the 2004 Presidential Election

2.5. Summary

pt. II ADDRESSING VIOLATIONS OF ASSUMPTIONS

3. Diagnostics For Unusual Observations

3.1. Introduction

3.2. Concepts and Background Material

3.3. Methodology

3.3.1. Residuals and Outliers

3.3.2. Leverage Points

3.3.3. Influential Points and Cook's Distance

3.4. Example

- Estimating Home Prices (continued)

3.5. Summary

4. 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

4.3.1. Logged Response Variable

4.3.2. Logged Predictor Variable

4.4. Example

- Predicting Movie Grosses After One Week

4.5. Summary

5. Time Series Data And Autocorrelation

5.1. Introduction

5.2. Concepts and Background Material

5.3. Methodology: Identifying Autocorrelation

5.3.1. The Durbin-Watson Statistic

5.3.2. The Autocorrelation Function (ACF)

5.3.3. Residual Plots and the Runs Test

5.4. Methodology: Addressing Autocorrelation

5.4.1. Detrending and Deseasonalizing

5.4.2. Example

- e-Commerce Retail Sales

5.4.3. Lagging and Differencing

5.4.4. Example

- Stock Indexes

5.4.5. Generalized Least Squares (GLS): The Cochrane-Orcutt Procedure

5.4.6. Example

- Time Intervals Between Old Faithful Geyser Eruptions

5.5. Summary

pt. III CATEGORICAL PREDICTORS

6. Analysis Of Variance

6.1. Introduction

6.2. Concepts and Background Material

6.2.1. One-Way ANOVA

6.2.2. Two-Way ANOVA

6.3. Methodology

6.3.1. Codings for Categorical Predictors

6.3.2. Multiple Comparisons

6.3.3. Levene's Test and Weighted Least Squares

6.3.4. Membership in Multiple Groups

6.4. Example

- DVD Sales of Movies

6.5. Higher-Way ANOVA

6.6. Summary

7. Analysis Of Covariance

7.1. Introduction

7.2. Methodology

7.2.1. Constant Shift Models

7.2.2. Varying Slope Models

7.3. Example

- International Grosses of Movies

7.4. Summary

pt. IV NON-GAUSSIAN REGRESSION MODELS

8. Logistic Regression

8.1. Introduction

8.2. Concepts and Background Material

8.2.1. The Logit Response Function

8.2.2. Bernoulli and Binomial Random Variables

8.2.3. Prospective and Retrospective Designs

8.3. Methodology

8.3.1. Maximum Likelihood Estimation

8.3.2. Inference, Model Comparison, and Model Selection

8.3.3. Goodness-of-Fit

8.3.4. Measures of Association and Classification Accuracy

8.3.5. Diagnostics

8.4. Example

- Smoking and Mortality

8.5. Example

- Modeling Bankruptcy

8.6. Summary

9. Multinomial Regression

9.1. Introduction

9.2. Concepts and Background Material

9.1.1. Nominal Response Variable

9.2.2. Ordinal Response Variable

9.3. Methodology

9.3.1. Estimation

9.3.2. Inference, Model Comparisons, and Strength of Fit

9.3.3. Lack of Fit and Violations of Assumptions

9.4. Example

- City Bond Ratings

9.5. Summary

10. Count Regression

10.1. Introduction

10.2. Concepts and Background Material

10.2.1. The Poisson Random Variable

10.2.2. Generalized Linear Models

10.3. Methodology

10.3.1. Estimation and Inference

10.3.2. Offsets

10.4. Overdispersion and Negative Binomial Regression

10.4.1. Quasi-likelihood

10.4.2. 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

10.7.1. Example

- International Grosses of Movies (continued)

10.8. Summary

11. Models For Time-To-Event (Survival) Data

11.1. Introduction

11.2. Concepts and Background Material

11.2.1. The Nature of Survival Data

11.2.2. Accelerated Failure Time Models

11.2.3. The Proportional Hazards Model

11.3. Methodology

11.3.1. The Kaplan-Meier Estimator and the Log-Rank Test

11.3.2. Parametric (Likelihood) Estimation

11.3.3. Semiparametric (Partial Likelihood) Estimation

11.3.4. The Buckley-James Estimator

11.4. Example

- The Survival of Broadway Shows (continued)

11.5. Left-Truncated/Right-Censored Data and Time-Varying Covariates

11.5.1. Left-Truncated/Right-Censored Data

11.5.2. Example

11.5.3. Time-Varying Covariates

11.5.4. Example

- Female Heads of Government

11.6. Summary

pt. V OTHER REGRESSION MODELS

12. Nonlinear Regression

12.1. Introduction

12.2. Concepts and Background Material

12.3. Methodology

12.3.1. Nonlinear Least Squares Estimation

12.3.2. Inference for Nonlinear Regression Models

12.4. Example

- Michaelis-Menten Enzyme Kinetics

12.5. Summary

13. Models For Longitudinal And Nested Data

13.1. Introduction

13.2. Concepts and Background Material

13.2.1. Nested Data and ANOVA

13.2.2. Longitudinal Data and Time Series

13.2.3. Fixed Effects Versus Random Effects

13.3. Methodology

13.3.1. The Linear Mixed Effects Model

13.3.2. The Generalized Linear Mixed Effects Model

13.3.3. Generalized Estimating Equations

13.3.4. Nonlinear Mixed Effects Models

13.4. Example

- Tumor Growth in a Cancer Study

13.5. Example

- Unprovoked Shark Attacks in the United States

13.6. Summary

14. Regularization Method's And Sparse Models

14.1. Introduction

14.2. Concepts and Background Material

14.2.1. The Bias-Variance Tradeoff

14.2.2. Large Numbers of Predictors and Sparsity

14.3. Methodology

14.3.1. Forward Stepwise Regression

14.3.2. Ridge Regression

14.3.3. The Lasso

14.3.4. Other Regularization Methods

14.3.5. Choosing the Regularization Parameter(s)

14.3.6. More Structured Regression Problems

14.3.7. Cautions About Regularization Methods

14.4. Example

- Human Development Index

14.5. Summary

pt. VI NONPARAMETRIC AND SEMIPARAMETRIC MODELS

15. Smoothing And Additive Models

15.1. Introduction

15.2. Concepts and Background Material

15.2.1. The Bias-Variance Tradeoff

15.2.2. Smoothing and Local Regression

15.3. Methodology

15.3.1. Local Polynomial Regression

15.3.2. Choosing the Bandwidth

15.3.3. Smoothing Splines

15.3.4. Multiple Predictors, the Curse of Dimensionality, and Additive Models

15.4. Example

- Prices of German Used Automobiles

15.5. Local and Penalized Likelihood Regression

15.5.1. Example

- The Bechdel Rule and Hollywood Movies

15.6. Using Smoothing to Identify Interactions

15.6.1. Example

15.7. Summary

16. Tree-Based Models

16.1. Introduction

16.2. Concepts and Background Material

16.2.1. Recursive Partitioning

16.2.2. Types of Trees

16.3. Methodology

16.3.1. CART

16.3.2. Conditional Inference Trees

16.3.3. Ensemble Methods

16.4. Examples

16.4.1. Estimating Home Prices (continued)

16.4.2. Example

- Courtesy in Airplane Travel

16.5. Trees for Other Types of Data

16.5.1. Trees for Nested and Longitudinal Data

16.5.2. Survival Trees

16.6. Summary.

Notes:

Revised edition of: Handbook of regression analysis. 2013.

Includes bibliographical references and index.

Other Format:

Online version: Chatterjee, Samprit, Handbook of regression analysis with applications in r

ISBN:

9781119392378

1119392373

OCLC:

1143839175

Publisher Number:

99987078154