- Hidden Truncation Normal Regression
- Hidden Truncation Normal Regression
- ㆍ 저자명
- Kim. Sungsu
- ㆍ 간행물명
- 한국통계학회 논문집
- ㆍ 권/호정보
- 2012년|19권 6호|pp.793-798 (6 pages)
- ㆍ 발행정보
- 한국통계학회
- ㆍ 파일정보
- 정기간행물|ENG| PDF텍스트
- ㆍ 주제분야
- 기타
이 논문은 한국과학기술정보연구원과 논문 연계를 통해 무료로 제공되는 원문입니다.
In this paper, we propose regression methods based on the likelihood function. We assume Arnold-Beaver Skew Normal(ABSN) errors in a simple linear regression model. It was shown that the novel method performs better with an asymmetric data set compared to the usual regression model with the Gaussian errors. The utility of a novel method is demonstrated through simulation and real data sets.