Sparse wavelet estimation in quantile regression with multiple functional predictors

Dengdeng Yu, Li Zhang, Ivan Mizera, Bei Jiang and Linglong Kong

Computational Statistics & Data Analysis, 2019, vol. 136, issue C, 12-29

Abstract: To study quantile regression in partial functional linear model where response is scalar and predictors include both scalars and multiple functions, wavelet basis are usually adopted to better approximate functional slopes while effectively detect local features. The sparse group lasso penalty is imposed to select important functional predictors while capture shared information among them. The estimation problem can be reformulated into a standard second-order cone program and then solved by an interior point method. A novel algorithm is proposed by using alternating direction method of multipliers (ADMM) which was recently employed by many researchers in solving penalized quantile regression problems. The asymptotic properties such as the convergence rate and prediction error bound have been established. Simulations and a real data from ADHD-200 fMRI data are investigated to show the superiority of our proposed method.

Keywords: Functional data analysis; Sparse group lasso; ADMM; Convergence rate; Prediction error bound; ADHD (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947318302810
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:136:y:2019:i:c:p:12-29

DOI: 10.1016/j.csda.2018.12.002

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().