Variable selection in doubly truncated regression

TL;DR

This paper introduces a new method for variable selection in doubly truncated regression models, addressing bias and enabling accurate estimation even with many predictors.

Contribution

It proposes a simultaneous estimation and variable selection procedure using a Mann-Whitney-type loss and adaptive LASSO, with theoretical guarantees and practical validation.

Findings

The method achieves consistency and asymptotic normality.

It demonstrates oracle property in variable selection.

Simulation studies confirm finite sample effectiveness.

Abstract

Doubly truncated data arise in many areas such as astronomy, econometrics, and medical studies. For the regression analysis with doubly truncated response variables, the existence of double truncation may bring bias for estimation as well as affect variable selection. We propose a simultaneous estimation and variable selection procedure for the doubly truncated regression, allowing a diverging number of regression parameters. To remove the bias introduced by the double truncation, a Mann-Whitney-type loss function is used. The adaptive LASSO penalty is then added into the loss function to achieve simultaneous estimation and variable selection. An iterative algorithm is designed to optimize the resulting objective function. We establish the consistency and the asymptotic normality of the proposed estimator. The oracle property of the proposed selection procedure is also obtained. Some simulation studies are conducted to show the finite sample performance of the proposed approach. We also apply the method to analyze a real astronomical data.