Penalized regression is one of the most popular statistical tools to analyze high dimensional data, where the number of features exceeds the number of observations. My research addresses two important questions in this active research area. First, we proposed a new cross-validation regime that improves the model fitting for penalized Cox regression. Second, based on a penalized regression fit, we developed a test statistic that improves the false discovery rate estimation of selected features.