Detailed Information

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

Bootstrapping least distance estimator in the multivariate regression model

Authors
Jhun, MyoungshicChoi, Inkyung
Issue Date
1-10월-2009
Publisher
ELSEVIER SCIENCE BV
Citation
COMPUTATIONAL STATISTICS & DATA ANALYSIS, v.53, no.12, pp.4221 - 4227
Indexed
SCIE
SCOPUS
Journal Title
COMPUTATIONAL STATISTICS & DATA ANALYSIS
Volume
53
Number
12
Start Page
4221
End Page
4227
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/119133
DOI
10.1016/j.csda.2009.05.012
ISSN
0167-9473
Abstract
The most popular estimation methods in multivariate linear regression are the multivariate least squares estimation and the multivariate least absolute estimation. Each method repeats its univariate estimation method p, the number of response variables, times. Although they are relatively easy to apply, they do not employ the relationship between response variables. This study considers the multivariate least distance estimator of Bai et al. (1990) that accounts for this relationship. We confirm its relative efficiency with respect to the multivariate least absolute estimator under the multivariate normal distribution and contaminated distribution. However, the asymptotic inference of the multivariate least distance estimator is shown to perform poorly in certain circumstances. We suggest the boot-strap method to infer the regression parameters and confirm its viability using Monte Carlo studies. (C) 2009 Elsevier B.V. All rights reserved.
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