Title
Kernel Resampling To Improve The Performance Of Multiple Regression With Small Samples
Keywords
Bootstrap; Estimation bias; Kernel method; Multiple regression; Resampling; Smoothing; Standard error
Abstract
The issues of estimation accuracy and statistical power in multiple regression with small samples have long been a concern. The present study utilizes a new multivariate resampling method, the kernel resampling technique (KRT), to improve the estimation accuracy and statistical power in multiple regression with small samples. KRT is a distribution-free method that employs kernel technique to create multivariate resamples based on a given small sample. The findings from both a simulation study and an empirical example suggest that the statistical performance of multiple regression has been improved through KRT. © 2010 Nova Science Publishers, Inc.
Publication Date
12-28-2010
Publication Title
Journal of Applied Statistical Science
Volume
17
Issue
4
Number of Pages
573-582
Document Type
Article
Personal Identifier
scopus
Copyright Status
Unknown
Socpus ID
78650430635 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/78650430635
STARS Citation
Bai, Haiyan, "Kernel Resampling To Improve The Performance Of Multiple Regression With Small Samples" (2010). Scopus Export 2010-2014. 121.
https://stars.library.ucf.edu/scopus2010/121