Ebook: Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis

Many texts are excellent sources of knowledge about individual statistical tools, but the art of data analysis is about choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for dealing with nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations," and powerful model validation techniques based on the bootstrap. This text realistically deals with model uncertainty and its effects on inference to achieve "safe data mining".

---

Many texts are excellent sources of knowledge about individual statistical tools, but the art of data analysis is about choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for dealing with nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations," and powerful model validation techniques based on the bootstrap. This text realistically deals with model uncertainty and its effects on inference to achieve "safe data mining".

---

Many texts are excellent sources of knowledge about individual statistical tools, but the art of data analysis is about choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for dealing with nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations," and powerful model validation techniques based on the bootstrap. This text realistically deals with model uncertainty and its effects on inference to achieve "safe data mining".
Content:
Front Matter....Pages i-xxiii
Introduction....Pages 1-9
General Aspects of Fitting Regression Models....Pages 11-40
Missing Data....Pages 41-52
Multivariable Modeling Strategies....Pages 53-85
Resampling, Validating, Describing, and Simplifying the Model....Pages 87-103
S-Plus Software....Pages 105-120
Case Study in Least Squares Fitting and Interpretation of a Linear Model....Pages 121-146
Case Study in Imputation and Data Reduction....Pages 147-177
Overview of Maximum Likelihood Estimation....Pages 179-213
Binary Logistic Regression....Pages 215-267
Logistic Model Case Study 1: Predicting Cause of Death....Pages 269-298
Logistic Model Case Study 2: Survival of Titanic Passengers....Pages 299-330
Ordinal Logistic Regression....Pages 331-343
Case Study in Ordinal Regression, Data Reduction, and Penalization....Pages 345-373
Models Using Nonparametric Transformations of X and Y ....Pages 375-388
Introduction to Survival Analysis....Pages 389-412
Parametric Survival Models....Pages 413-442
Case Study in Parametric Survival Modeling and Model Approximation....Pages 443-464
Cox Proportional Hazards Regression Model....Pages 465-507
Case Study in Cox Regression....Pages 509-522
Back Matter....Pages 523-571

---

Many texts are excellent sources of knowledge about individual statistical tools, but the art of data analysis is about choosing and using multiple tools. Instead of presenting isolated techniques, this text emphasizes problem solving strategies that address the many issues arising when developing multivariable models using real data and not standard textbook examples. It includes imputation methods for dealing with missing data effectively, methods for dealing with nonlinear relationships and for making the estimation of transformations a formal part of the modeling process, methods for dealing with "too many variables to analyze and not enough observations," and powerful model validation techniques based on the bootstrap. This text realistically deals with model uncertainty and its effects on inference to achieve "safe data mining".
Content:
Front Matter....Pages i-xxiii
Introduction....Pages 1-9
General Aspects of Fitting Regression Models....Pages 11-40
Missing Data....Pages 41-52
Multivariable Modeling Strategies....Pages 53-85
Resampling, Validating, Describing, and Simplifying the Model....Pages 87-103
S-Plus Software....Pages 105-120
Case Study in Least Squares Fitting and Interpretation of a Linear Model....Pages 121-146
Case Study in Imputation and Data Reduction....Pages 147-177
Overview of Maximum Likelihood Estimation....Pages 179-213
Binary Logistic Regression....Pages 215-267
Logistic Model Case Study 1: Predicting Cause of Death....Pages 269-298
Logistic Model Case Study 2: Survival of Titanic Passengers....Pages 299-330
Ordinal Logistic Regression....Pages 331-343
Case Study in Ordinal Regression, Data Reduction, and Penalization....Pages 345-373
Models Using Nonparametric Transformations of X and Y ....Pages 375-388
Introduction to Survival Analysis....Pages 389-412
Parametric Survival Models....Pages 413-442
Case Study in Parametric Survival Modeling and Model Approximation....Pages 443-464
Cox Proportional Hazards Regression Model....Pages 465-507
Case Study in Cox Regression....Pages 509-522
Back Matter....Pages 523-571
....