Open Access
February 2016 Partially linear additive quantile regression in ultra-high dimension
Ben Sherwood, Lan Wang
Ann. Statist. 44(1): 288-317 (February 2016). DOI: 10.1214/15-AOS1367


We consider a flexible semiparametric quantile regression model for analyzing high dimensional heterogeneous data. This model has several appealing features: (1) By considering different conditional quantiles, we may obtain a more complete picture of the conditional distribution of a response variable given high dimensional covariates. (2) The sparsity level is allowed to be different at different quantile levels. (3) The partially linear additive structure accommodates nonlinearity and circumvents the curse of dimensionality. (4) It is naturally robust to heavy-tailed distributions. In this paper, we approximate the nonlinear components using B-spline basis functions. We first study estimation under this model when the nonzero components are known in advance and the number of covariates in the linear part diverges. We then investigate a nonconvex penalized estimator for simultaneous variable selection and estimation. We derive its oracle property for a general class of nonconvex penalty functions in the presence of ultra-high dimensional covariates under relaxed conditions. To tackle the challenges of nonsmooth loss function, nonconvex penalty function and the presence of nonlinear components, we combine a recently developed convex-differencing method with modern empirical process techniques. Monte Carlo simulations and an application to a microarray study demonstrate the effectiveness of the proposed method. We also discuss how the method for a single quantile of interest can be extended to simultaneous variable selection and estimation at multiple quantiles.


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Ben Sherwood. Lan Wang. "Partially linear additive quantile regression in ultra-high dimension." Ann. Statist. 44 (1) 288 - 317, February 2016.


Received: 1 September 2014; Revised: 1 July 2015; Published: February 2016
First available in Project Euclid: 10 December 2015

zbMATH: 1331.62264
MathSciNet: MR3449769
Digital Object Identifier: 10.1214/15-AOS1367

Primary: 62G35
Secondary: 62G20

Keywords: high dimensional data , nonconvex penalty , partial linear , Quantile regression , Variable selection

Rights: Copyright © 2016 Institute of Mathematical Statistics

Vol.44 • No. 1 • February 2016
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