Regression Modeling Strategies

The book will serve as a reference for data analysts and statistical methodologists.

Written by a well-known biostatistical researcher and expert on S-Plus Provides full-scale case studies of non-trivial datasets Case studies use S-PLUS Includes supplementary material: sn.pub/extras

Autorentext
The book will serve as a reference for data analysts and statistical methodologists.

Inhalt
1 Introduction.- 2 General Aspects of Fitting Regression Models.- 3 Missing Data.- 4 Multivariable Modeling Strategies.- 5 Resampling, Validating, Describing, and Simplifying the Model.- 6 S-Plus Software.- 7 Case Study in Least Squares Fitting and Interpretation of a Linear Model.- 8 Case Study in Imputation and Data Reduction.- 9 Overview of Maximum Likelihood Estimation.- 10 Binary Logistic Regression.- 11 Logistic Model Case Study 1: Predicting Cause of Death.- 12 Logistic Model Case Study 2: Survival of Titanic Passengers.- 13 Ordinal Logistic Regression.- 14 Case Study in Ordinal Regression, Data Reduction, and Penalization.- 15 Models Using Nonparametric Transformations of X and Y.- 16 Introduction to Survival Analysis.- 17 Parametric Survival Models.- 18 Case Study in Parametric Survival Modeling and Model Approximation.- 19 Cox Proportional Hazards Regression Model.- 20 Case Study in Cox Regression.