Adaptive LASSO model selection in a multiphase quantile regression

TL;DR

This paper introduces an adaptive LASSO approach for multiphase quantile regression that effectively performs variable selection and change-point detection without prior knowledge of error moments, demonstrating superior performance through simulations.

Contribution

The paper develops a novel adaptive LASSO method for quantile regression with change-points, proving oracle properties and providing convergence rates, applicable even when phases are unknown.

Findings

The method satisfies oracle properties for variable selection.

Convergence rates for change-point and parameter estimators are established.

Numerical results show superior performance over existing methods.

Abstract

We propose a general adaptive LASSO method for a quantile regression model. Our method is very interesting when we know nothing about the first two moments of the model error. We first prove that the obtained estimators satisfy the oracle properties, which involves the relevant variable selection without using hypothesis test. Next, we study the proposed method when the (multiphase) model changes to unknown observations called change-points. Convergence rates of the change-points and of the regression parameters estimators in each phase are found. The sparsity of the adaptive LASSO quantile estimators of the regression parameters is not affected by the change-points estimation. If the phases number is unknown, a consistent criterion is proposed. Numerical studies by Monte Carlo simulations show the performance of the proposed method, compared to other existing methods in the literature, for models with a single phase or for multiphase models. The adaptive LASSO quantile method performs better than known variable selection methods, as the least squared method with adaptive LASSO penalty, $L_1$-method with LASSO-type penalty and quantile method with SCAD penalty.