Suchergebnisse

Cover -- Half Title -- Series -- Title -- Copyright -- Contents -- 1 R -- The R Console and R Scripts -- R Libraries -- Reading and Viewing Data in R -- Missing Data -- Variable and Data Set Types -- Descriptive Statistics and Graphics in R -- Summary -- 2 Theoretical Underpinnings of Regularization Methods -- The Need for Variable Selection Methods -- The Lasso Estimator -- The Ridge Estimator -- The Elastic Net Estimator -- The Adaptive Lasso -- The Group Lasso -- Bayesian Regularization -- Inference for Regularization Methods -- Summary -- 3 Regularization Methods for Linear Models -- Linear Regression -- Fitting Linear Regression Model with R -- Assessing Regression Assumptions Using R -- Variable Selection without Regularization -- Stepwise Regression -- Application of Stepwise Regression Using R -- Best Subsets Regression -- Application of Best Subsets Regression Using R -- Regularized Linear Regression -- Lasso Regression -- Ridge Regression -- Elastic Net Regression -- Bayesian Lasso Regression -- Bayesian Ridge Regression -- Adaptive Lasso Regression -- Group Lasso Regression -- Comparison of Modeling Approaches -- Summary -- 4 Regularization Methods for Generalized Linear Models -- Logistic Regression for Dichotomous Outcome -- Fitting Logistic Regression with R -- Regularization with Logistic Regression for Dichotomous Outcomes -- Logistic Regression with the Lasso Penalty -- Logistic Regression with the Ridge Penalty -- Penalized Logistic Regression with the Bayesian Estimator -- Adaptive Lasso for Dichotomous Logistic Regression -- Grouped Regularization for Dichotomous Logistic Regression -- Logistic Regression for Ordinal Outcome -- Regularized Ordinal Logistic Regression -- Regression Models for Count Data -- Regularized Count Regression -- Cox Proportional Hazards Model -- Regularized Cox Regression -- Summary.