Robust shrinkage estimation and selection for functional multiple linear model through LAD loss

Lele Huang, Junlong Zhao, Huiwen Wang and Siyang Wang

Computational Statistics & Data Analysis, 2016, vol. 103, issue C, 384-400

Abstract: In functional data analysis (FDA), variable selection in regression model is an important issue when there are multiple functional predictors. Most of the existing methods are based on least square loss and consequently sensitive to outliers in error. Robust variable selection procedure is desirable. When functional predictors are considered, both non-data-driven basis (e.g. B-spline) and data-driven basis (e.g. functional principal component (FPC)) are commonly used. The data-driven basis is flexible and adaptive, but it raise some difficulties, since the basis must be estimated from data.

Keywords: Functional linear model; Variables selection; Robustness; Multiple functional predictors (search for similar items in EconPapers)
Date: 2016
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/S0167947316301293
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:103:y:2016:i:c:p:384-400

DOI: 10.1016/j.csda.2016.05.017

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 ().