Pretest and Stein-Type Estimations in Quantile Regression Model

TL;DR

This paper explores pretest and Stein-type shrinkage estimators to enhance quantile regression, especially when classical assumptions are violated, and compares their performance with Lasso, Ridge, and Elastic Net through simulations and real data.

Contribution

It introduces novel pretest and shrinkage estimation methods for quantile regression and evaluates their effectiveness against existing regularization techniques.

Findings

Proposed estimators outperform classical methods under assumption violations.

Simulation results show improved estimation accuracy with the new methods.

Real data applications demonstrate practical advantages of the proposed estimators.

Abstract

In this study, we consider preliminary test and shrinkage estimation strategies for quantile regression models. In classical Least Squares Estimation (LSE) method, the relationship between the explanatory and explained variables in the coordinate plane is estimated with a mean regression line. In order to use LSE, there are three main assumptions on the error terms showing white noise process of the regression model, also known as Gauss-Markov Assumptions, must be met: (1) The error terms have zero mean, (2) The variance of the error terms is constant and (3) The covariance between the errors is zero i.e., there is no autocorrelation. However, data in many areas, including econometrics, survival analysis and ecology, etc. does not provide these assumptions. First introduced by Koenker, quantile regression has been used to complement this deficiency of classical regression analysis and to improve the least square estimation. The aim of this study is to improve the performance of quantile regression estimators by using pre-test and shrinkage strategies. A Monte Carlo simulation study including a comparison with quantile $L_1$--type estimators such as Lasso, Ridge and Elastic Net are designed to evaluate the performances of the estimators. Two real data examples are given for illustrative purposes. Finally, we obtain the asymptotic results of suggested estimators