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Screening and Clustering of Sparse Regressions with Finite Non-Gaussian Mixtures

Zhang, Jian (2016) Screening and Clustering of Sparse Regressions with Finite Non-Gaussian Mixtures. Biometrics, 73 (2). pp. 540-550. ISSN 0006-341X. (doi:10.1111/biom.12585) (KAR id:57099)

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http://dx.doi.org/10.1111/biom.12585

Abstract

This article proposes a method to address the problem that can arise when covariates

errors, or when a true mixture of regressions produced the data. The method begins with non-

mixture regression model to the selected data with help of a new penalization scheme. Under certain

even when the population is heterogeneous. We further prove that there exists an elbow-point in

the model. By simulations, we demonstrate that the new procedure can substantially improve the

applying the proposed procedure to motif data analysis in molecular biology, we demonstrate that

the new method holds promise in practice.

Item Type: Article
DOI/Identification number: 10.1111/biom.12585
Projects: [UNSPECIFIED] High dimensional inferences with applications
Uncontrolled keywords: Heterogeneity, non-Gaussian mixture regression models, component-wise regularization, simultaneous clustering and variable screening.
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Jian Zhang
Date Deposited: 05 Sep 2016 16:21 UTC
Last Modified: 17 Jan 2020 16:37 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/57099 (The current URI for this page, for reference purposes)
Zhang, Jian: https://orcid.org/0000-0001-8405-2323
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