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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2017.9
- 수록면
- 1,191 - 1,204 (14page)
이용수
초록· 키워드
오류제보하기
Weighted least squares (WLS) estimation is often easily used for the data with heteroscedastic errors because it is intuitive and computationally inexpensive. However, WLS estimator is less robust to a few outliers and sometimes it may be inefficient. In order to overcome robustness problems, Box-Cox transformation, Huber`s M estimation, bisquare estimation, and Yohai`s MM estimation have been proposed. Also, more efficient estimations than WLS have been suggested such as Bayesian methods (Cepeda and Achcar, 2009) and semiparametric methods (Kim and Ma, 2012) in heteroscedastic error models. Recently, Çelik (2015) proposed the weight methods applicable to the heteroscedasticity patterns including butterfly-distributed residuals and megaphone-shaped residuals. In this paper, we review heteroscedastic regression estimators related to robust or efficient estimation and describe their properties. Also, we analyze cost data of U.S. Electricity Producers in 1955 using the methods discussed in the paper.
목차
Abstract
1. Introduction
2. Methods
3. Data analysis
4. Conclusion
References
참고문헌 (15)
참고문헌 신청
Box, G. E. and Cox, D. R. (1964). An analysis of transformations. Journal of the Royal Statistical Society Series B (Methodological), 211-252.
C elik, R. (2015). Stabilizing heteroscedasticity for butterfly-distributed residuals by the weighting absolute centered external variable. Journal of Applied Statistics, 42, 705-721.
Cepeda, E. and Gamerman, D. (2000). Bayesian modeling of variance heterogeneity in normal regression models. Brazilian Journal of Probability and Statistics, 207-221.
Cepeda, E. and Gamerman, D. (2005). Bayesian methodology for modeling parameters in the two parameter exponential family. Revista Estadstica, 57, 93-105.
Cepeda-Cuervo, E. (2015). Beta regression models: Joint mean and variance modeling. Journal of Statistical Theory and Practice, 9, 134-145.
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UCI(KEPA) : I410-ECN-0101-2018-041-001796483