Variable selection and transformation in linear regression models

Citations

WEB OF SCIENCE

4
Citations

SCOPUS

2

초록

We develop a method for comparing separate linear models, for a common response variable that may be expressed on different scales and may be described by distinct explanatory variables. A method of stochastic simulation is used to approximate the fitted maximum likelihood estimates and then the Cox statistic is computed to test separate linear models. The bootstrap iteration is also used to calibrate confidence intervals to correct the test level. (c) 2005 Elsevier B.V. All rights reserved.

키워드

bootstrap calibration; cox statistic; Kullback-Leibler information; Monte Carlo estimation; parametric transformation; TESTING SEPARATE FAMILIES; HYPOTHESES; NORMALITY
제목
Variable selection and transformation in linear regression models
저자
Yeo, IK
DOI
10.1016/j.spl.2004.12.018
발행일
2005-05
유형
Article
저널명
Statistics and Probability Letters
권
72
호
3
페이지
219 ~ 226