A simple model‐based approach to variable selection in classification and clustering. Issue 2 (20th February 2015)

• Record Type: Journal Article
• Authors: Partovi Nia, Vahid; Davison, Anthony C.
• Is Part Of: Canadian journal of statistics. Volume 43:Issue 2(2015)
• Page Start: 157
• Page End: 175
• Publication Date: 2015-02-20
• Subjects: Mathematical statistics -- Periodicals; 519.5
• DOI: 10.1002/cjs.11241
• Languages: English
• ISSNs: 0319-5724

Abstract

<abstract abstract-type="main" xml:lang="en"> <title>Abstract</title> <sec id="cjs11241-sec-0001" sec-type="section"> <p>Clustering and classification of replicated data is often performed using classical techniques that inappropriately treat the data as unreplicated, or by complex modern ones that are computationally demanding. In this paper, we introduce a simple approach based on a "spike‐and‐slab" mixture model that is fast, automatic, allows classification, clustering and variable selection in a single framework, and can handle replicated or unreplicated data. Simulation shows that our approach compares well with other recently proposed methods. The ideas are illustrated by application to microarray and metabolomic data. <italic>The Canadian Journal of Statistics</italic> 43: 157–175; 2015 © 2015 Statistical Society of Canada</p> </sec> </abstract>