Distance Measures in Multivariate Constrained Regression

• 발행기관: 한국자료분석학회
• 저자명: 김명근(Myung Geun Kim)
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.10 No.2, 693~700쪽, 전체 8쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2008.04.30

영문 초록

A problem of detecting influential observations in multivariate constrained regression is considered. Two types of distance measures are found. Cook s distance introduced in the ordinary univariate linear regression is adapted to multivariate regression with linear constraints on regression coefficients and its explicit form will be derived. Likelihood distances for multivariate constrained regression will be computed for two cases: one is the case in which both the regression coefficients and the covariance matrix are of interest and the other is the case in which only the regression coefficients are of interest. A numerical example is given for illustration.

목차

1. Introduction

2. Preliminaries

3. Cook s Distance

4. Likelihood Distances

5. A numerical example

References

키워드

Cook s distance likelihood distance linear constraints multivariate regression