Elastic Net Regularization Paths for All Generalized Linear Models

Generalized Linear Model for Gamma Distributed Variables via Elastic Net Regularization

The Generalized Linear Model (GLM) for the Gamma distribution (glmGamma) is widely used in modeling continuous, non-negative and positive-skewed data, such as insurance claims and survival data. However, model selection for GLM depends on AIC/BIC criteria, which is computationally impractical for even a moderate number of variables. In this paper, we develop variable selection for glmGamma using elastic net regularization (glmGammaNet), for which we provide an algorithm and implementation. The glmGammaNet model is more challening than other more common GLMs as the likelihood function has no gl…

Regularized Ordinal Regression and the ordinalNet R Package

Regularization techniques such as the lasso (Tibshirani 1996) and elastic net (Zou and Hastie 2005) can be used to improve regression model coefficient estimation and prediction accuracy, as well as to perform variable selection. Ordinal regression models are widely used in applications where the use of regularization could be beneficial; however, these models are not included in many popular software packages for regularized regression. We propose a coordinate descent algorithm to fit a broad class of ordinal regression models with an elastic net penalty. Furthermore, we demonstrate that each…

通过加权正则化实现稳健的全局弗雷歇回归

Robust Global Fr'echet Regression via Weight Regularization

本文提出了一种通过加权正则化实现稳健全局弗雷歇回归的方法，结合弹性网络正则化和贝叶斯信息准则，提升对异常值的鲁棒性。

ensr: R Package for Simultaneous Selection of Elastic Net Tuning Parameters

Motivation: Elastic net regression is a form of penalized regression that lies between ridge and least absolute shrinkage and selection operator (LASSO) regression. The elastic net penalty is a powerful tool controlling the impact of correlated predictors and the overall complexity of generalized linear regression models. The elastic net penalty has two tuning parameters: $λ$ for the complexity and $α$ for the compromise between LASSO and ridge. The R package glmnet provides efficient tools for fitting elastic net models and selecting $λ$ for a given $α.$ However, glmnet does not simultaneousl…

一种基于块坐标下降的用于组Lasso和弹性网惩罚回归的快速可扩展路径求解器

A Fast and Scalable Pathwise-Solver for Group Lasso and Elastic Net Penalized Regression via Block-Coordinate Descent

本研究提出基于块坐标下降的快速可扩展算法，结合Newton法与自适应二分法求解组Lasso及组弹性网正则化路径，开发的adelie包速度比同类最快工具快3-10倍，且lasso求解性能媲美glmnet。