Penalized likelihood regression consists of a category of commonly used regularization methods, including regression splines with RKHS penalty and the LASSO. When the observed data comes from a non-Gaussian exponential family distribution, a penalized log-likelihood is commonly used to estimate of the regression function. This technique allows a flexible form of the estimator and aims at an app...

This article is concerned with the Bridge Regression, which a special family in penalized regression penalty function $\sum_{j=1}^{p}|\beta_j|^q$ $q>0$, linear model restrictions. The proposed restricted bridge (RBRIDGE) estimator simultaneously estimates parameters and selects important variables when prior information about are available either low dimensional or high case. Using local quadra...

In this paper we purpose a blockwise descent algorithm for group-penalized multiresponse regression. Using a quasi-newton framework we extend this to group-penalized multinomial regression. We give the first publicly available implementation for these in R, and compare the speed of this algorithm to a similar algorithm for standard `1-penalized multinomial regression on simulated data — we show...

Reduction of the high dimensional classification using penalized logistic regression is one of the challenges in applying binary logistic regression. The applied penalized method, correlation based elastic penalty (CBEP), was used to overcome the limitation of LASSO and elastic net in variable selection when there are perfect correlation among explanatory variables. The performance of the CBEP ...