The use of random-effect models for high-dimensional variable selection problems

Sunghoon Kwon, Seungyoung Oh and Youngjo Lee

Computational Statistics & Data Analysis, 2016, vol. 103, issue C, 401-412

Abstract: We study the use of random-effect models for variable selection in high-dimensional generalized linear models where the number of covariates exceeds the sample size. Certain distributional assumptions on the random effects produce a penalty that is non-convex and unbounded at the origin. We introduce a unified algorithm that can be applied to various statistical models including generalized linear models. Simulation studies and data analysis are provided.

Keywords: Generalized linear model; Hierarchical likelihood; High-dimension; Random effect; Unbounded penalty; Variable selection (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947316301281
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:401-412

DOI: 10.1016/j.csda.2016.05.016

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 ().