Oracle Estimation of a Change Point in High-Dimensional Quantile Regression

• 成果类型: Article
• 署名作者: Lee, Sokbae; Liao, Yuan; Seo, Myung Hwan; Shin, Youngki
• 署名单位: Columbia University; University of London; London School Economics & Political Science; Rutgers University System; Rutgers University New Brunswick; Seoul National University (SNU); University of Technology Sydney; McMaster University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2017.1319840
• 发表日期: 2018
• 页码: 1184-1194
• 关键词: nonconcave penalized likelihood; threshold; Lasso; MODEL; Consistency; inference; selection; rates

摘要

In this article, we consider a high-dimensional quantile regression model where the sparsity structure may differ between two sub-populations. We develop (1)-penalized estimators of both regression coefficients and the threshold parameter. Our penalized estimators not only select covariates but also discriminate between a model with homogenous sparsity and a model with a change point. As a result, it is not necessary to know or pretest whether the change point is present, or where it occurs. Our estimator of the change point achieves an oracle property in the sense that its asymptotic distribution is the same as if the unknown active sets of regression coefficients were known. Importantly, we establish this oracle property without a perfect covariate selection, thereby avoiding the need for the minimum level condition on the signals of active covariates. Dealing with high-dimensional quantile regression with an unknown change point calls for a new proof technique since the quantile loss function is nonsmooth and furthermore the corresponding objective function is nonconvex with respect to the change point. The technique developed in this article is applicable to a general M-estimation framework with a change point, which may be of independent interest. The proposed methods are then illustrated via Monte Carlo experiments and an application to tipping in the dynamics of racial segregation. Supplementary materials for this article are available online.