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Generalized plaid models

Zhang, Jian (2012) Generalized plaid models. Neurocomputing, 79 (1). pp. 95-104. ISSN 0925-2312. (doi:10.1016/j.neucom.2011.10.011) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:31588)

Language: English

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The problem of two-way clustering has attracted considerable attention in diverse research areas such as functional genomics, text mining, and market research, where people want to simultaneously cluster rows and columns of a data matrix. In this paper, we propose a family of generalized plaid models for two-way clustering, where the layer estimation is regularized by Bayesian Information Criterion (BIC).

specifying the variance function to make the models adaptive to the entire distribution of the error term. A formal test is provided for finding significant layers. A Metropolis algorithm is also developed to calculate the maximum likelihood estimators of unknown parameters in the proposed models. Three simulation studies and the applications to two real datasets are reported, which demonstrate that our procedure is promising.

Item Type: Article
DOI/Identification number: 10.1016/j.neucom.2011.10.011
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: 11 Oct 2012 17:55 UTC
Last Modified: 13 Feb 2020 04:04 UTC
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