Joint Modeling for Mean Vector and Covariance Estimation with l1-PenaltyJoint Modeling for Mean Vector and Covariance Estimation with l1-Penalty
- Other Titles
- Joint Modeling for Mean Vector and Covariance Estimation with l1-Penalty
- Authors
- 정재환; 이정준; 김성환; 구자용
- Issue Date
- 2017
- Publisher
- 계명대학교 자연과학연구소
- Keywords
- Cholesky decomposition; Coordinate descent algorithm; Lasso; Penalized likelihood; Variable selection
- Citation
- Quantitative Bio-Science, v.36, no.1, pp.33 - 38
- Indexed
- KCI
OTHER
- Journal Title
- Quantitative Bio-Science
- Volume
- 36
- Number
- 1
- Start Page
- 33
- End Page
- 38
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/132371
- DOI
- 10.22283/qbs.2017.36.1.33
- ISSN
- 2288-1344
- Abstract
- In this study, we develop a novel updating-based method for penalized estimators for the mean vector and the covariance matrix. With a linear combination of predictors, the coefficients can be estimated by maximizing a penalized log likelihood function, and using coordinate descent algorithm is used to handle the l1-penalized function. In order to estimate the inverse covariance matrix estimation, we adopt a modified Cholesky decomposition so that to guarantee the positive definiteness of the estimators. In the genomic data analysis setting, we show that the proposed method can be efficiently used to detect the conditional independence among a group of genes, while adjusting for shared genetic effects. Simulation experiments benchmark the performance of the proposed method against another existing method.
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Collections - College of Political Science & Economics > Department of Statistics > 1. Journal Articles
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