Variable Selection Incorporating Prior Constraint Information into Lasso

TL;DR

This paper introduces a variable selection method that integrates prior constraint information into the lasso, improving model accuracy and efficiency across various statistical models.

Contribution

It presents a novel approach to incorporate prior constraints into lasso, applicable to multiple models, with strong theoretical backing and practical implementation strategies.

Findings

Enhanced variable selection accuracy

Applicable to generalized linear, Cox, and quantile regression models

Maintains good theoretical properties and efficiency

Abstract

We propose the variable selection procedure incorporating prior constraint information into lasso. The proposed procedure combines the sample and prior information, and selects significant variables for responses in a narrower region where the true parameters lie. It increases the efficiency to choose the true model correctly. The proposed procedure can be executed by many constrained quadratic programming methods and the initial estimator can be found by least square or Monte Carlo method. The proposed procedure also enjoys good theoretical properties. Moreover, the proposed procedure is not only used for linear models but also can be used for generalized linear models({\sl GLM}), Cox models, quantile regression models and many others with the help of Wang and Leng (2007)'s LSA, which changes these models as the approximation of linear models. The idea of combining sample and prior constraint information can be also used for other modified lasso procedures. Some examples are used for illustration of the idea of incorporating prior constraint information in variable selection procedures.