Fitting Penalized Estimator for Sparse Covariance Matrix with Left-Censored Data by the EM Algorithm

Estimating the sparse covariance matrix can effectively identify important features and patterns, and traditional estimation methods require complete data vectors on all subjects. When data are left-censored due to detection limits, common strategies such as excluding censored individuals or replaci...

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Autors principals:	Shanyi Lin, Qian-Zhen Zheng, Laixu Shang, Ping-Feng Xu, Man-Lai Tang
Format:	Artigo
Idioma:	Inglês
Publicat:	MDPI AG 2025-01-01
Col·lecció:	Mathematics
Matèries:	sparse covariance matrix Expectation Maximization algorithm penalized estimator left-censored data
Accés en línia:	https://www.mdpi.com/2227-7390/13/3/423
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