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

摘要: Introduction * General Aspects of Fitting Regression Models Missing Data Multivariable Modeling Strategies Resampling, Validating, Describing, and Simplifying the Model S-PLUS Software Case Study in Least Squares Interpretation a Linear Imputation Reduction Overview Maximum Likelihood Estimation Binary Logistic 1: Predicting Cause Death 2: Survival Titanic Passengers Ordinal Regrssion, Reduction, Penalization Using Nonparametic Transformations X Y to Analysis Parametric Approximation Cox Proportional Hazards