Composite Likelihood Inference in a Discrete Latent Variable Model for Two-Way "Clustering-by-Segmentation" Problems. Issue 2 (3rd April 2017)

• Record Type: Journal Article
• Authors: Bartolucci, Francesco; Chiaromonte, Francesca; Don, Prabhani Kuruppumullage; Lindsay, Bruce G.
• Is Part Of: Journal of computational and graphical statistics. Volume 26:Issue 2(2017)
• Page Start: 388
• Page End: 402
• Publication Date: 2017-04-03
• Subjects: Composite likelihood -- Cross-validation -- Crossed-effects models -- EM algorithm -- Finite mixture models -- Genomics; Mathematical statistics -- Data processing -- Periodicals; Mathematical statistics -- Graphic methods -- Periodicals; 519.50285
• DOI: 10.1080/10618600.2016.1172018
• Languages: English
• ISSNs: 1061-8600

Abstract

ABSTRACT: We consider a discrete latent variable model for two-way data arrays, which allows one to simultaneously produce clusters along one of the data dimensions (e.g., exchangeable observational units or features) and contiguous groups, or segments, along the other (e.g., consecutively ordered times or locations). The model relies on a hidden Markov structure but, given its complexity, cannot be estimated by full maximum likelihood. Therefore, we introduce a composite likelihood methodology based on considering different subsets of the data. The proposed approach is illustrated by simulation, and with an application to genomic data.