Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10139312 | Computational Statistics & Data Analysis | 2019 | 13 Pages |
Abstract
Quantile regression for functional partially linear model in ultra-high dimensions is proposed and studied. By focusing on the conditional quantiles, where conditioning is on both multiple random processes and high-dimensional scalar covariates, the proposed model can lead to a comprehensive description of the scalar response. To select and estimate important variables, a double penalized functional quantile objective function with two nonconvex penalties is developed, and the optimal tuning parameters involved can be chosen by a two-step technique. Based on the difference convex analysis (DCA), the asymptotic properties of the resulting estimators are established, and the convergence rate of the prediction of the conditional quantile function can be obtained. Simulation studies demonstrate a competitive performance against the existing approach. A real application to Alzheimer's Disease Neuroimaging Initiative (ADNI) data is used to illustrate the practicality of the proposed model.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Haiqiang Ma, Ting Li, Hongtu Zhu, Zhongyi Zhu,