Test Using Profile Empirical Likelihood Ratio with the Least Squares Estimator

• 발행기관: 한국자료분석학회
• 저자명: Sangjin Lee Changkon Hong
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.15 No.1, 31~40쪽, 전체 10쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2013.02.28

영문 초록

In regression analysis the test and interval estimation using the F statistic works only if the normality assumption on the error terms is valid. Without the normality assumption, a nonparametric method which does not need the distributional assumption will be preferred. The nonparametric construction of confidence region using empirical likelihood ratio was developed by Owen (1991). Hong, Jung (2006) proposed a profile empirical likelihood ratio statistic, which is simpler than but as efficient as the Owen’s. In this paper, we suggest a new test statistic using a profile empirical likelihood ratio for general testing problem in linear models and derive the asymptotic distribution of the statistic. We compare and the suggested statistic with the statistic in Hong, Jung (2006) via simulation study. The simulation results show that the new test statistic has higher rejection ratios than that of Hong, Jung (2006) and also show that the corresponding confidence intervals are narrower.

목차

1. Introduction

2. Empirical likelihood ratio

3. Empirical likelihood ratio test in linear model

4. Simulation study

5. Conclusions

References

키워드

confidence intervals inverse prediction profile empirical likelihood ratio using LSE rejection ratio