Mixtures of regression models with incomplete and noisy data
- Authors
- Jung, Byoung Cheol; Cheon, Sooyoung; Lim, Hwa Kyung
- Issue Date
- 2018
- Publisher
- TAYLOR & FRANCIS INC
- Keywords
- EM algorithm; Maximum likelihood; Missing values; Mixtures of regression models; Outliers
- Citation
- COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, v.47, no.2, pp.444 - 463
- Indexed
- SCIE
SCOPUS
- Journal Title
- COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
- Volume
- 47
- Number
- 2
- Start Page
- 444
- End Page
- 463
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/81019
- DOI
- 10.1080/03610918.2017.1283700
- ISSN
- 0361-0918
- Abstract
- The estimation of the mixtures of regression models is usually based on the normal assumption of components and maximum likelihood estimation of the normal components is sensitive to noise, outliers, or high-leverage points. Missing values are inevitable in many situations and parameter estimates could be biased if the missing values are not handled properly. In this article, we propose the mixtures of regression models for contaminated incomplete heterogeneous data. The proposed models provide robust estimates of regression coefficients varying across latent subgroups even under the presence of missing values. The methodology is illustrated through simulation studies and a real data analysis.
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Collections - Graduate School > Department of Applied Statistics > 1. Journal Articles
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