Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Efficient information-based criteria for model selection in quantile regressionEfficient information-based criteria for model selection in quantile regression

Other Titles
Efficient information-based criteria for model selection in quantile regression
Authors
Shin, WooyoungKim, MingangJung, Yoonsuh
Issue Date
3월-2022
Publisher
SPRINGER HEIDELBERG
Keywords
AIC; BIC; Check loss; GCV; Model validation; Quantile regression
Citation
JOURNAL OF THE KOREAN STATISTICAL SOCIETY, v.51, no.1, pp.245 - 281
Indexed
SCIE
SCOPUS
KCI
OTHER
Journal Title
JOURNAL OF THE KOREAN STATISTICAL SOCIETY
Volume
51
Number
1
Start Page
245
End Page
281
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/137945
DOI
10.1007/s42952-021-00137-1
ISSN
1226-3192
Abstract
Information-based model selection criteria such as the AIC and BIC employ check loss functions to measure the goodness of fit for quantile regression models. Model selection using a check loss function is robust due to its resistance to outlying observations. In the present study, we suggest modifying the check loss function to achieve a more efficient goodness of fit. Because the cusp of the check loss is quadratically adjusted in the modified version, greater efficiency (or variance reduction) in the model selection is expected. Because we focus on model selection here, we do not modify the model-fitting process. Generalized cross-validation is another common method for choosing smoothing parameters in quantile smoothing splines. We describe how this can be adjusted using the modified check loss to increase efficiency. The proposed generalized cross-validation is designed to reflect the target quantile and sample size. Two real data sets and simulation studies are presented to evaluate its performance using linear and nonlinear quantile regression models.
Files in This Item
There are no files associated with this item.
Appears in
Collections
College of Political Science & Economics > Department of Statistics > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Altmetrics

Total Views & Downloads

BROWSE