Multivariate methods using mixtures: Correspondence analysis, scaling and pattern-detection

Shirley Pledger and Richard Arnold

Computational Statistics & Data Analysis, 2014, vol. 71, issue C, 241-261

Abstract: Matrices of binary or count data are modelled under a unified statistical framework using finite mixtures to group the rows and/or columns. These likelihood-based one-mode and two-mode fuzzy clusterings provide maximum likelihood estimation of parameters and the options of using likelihood ratio tests or information criteria for model comparison. Geometric developments focused on pattern detection give likelihood-based analogues of various techniques in multivariate analysis, including multidimensional scaling, association analysis, ordination, correspondence analysis, and the construction of biplots. Illustrative examples demonstrate the effectiveness of these visualisations for identifying patterns of ecological significance (e.g. abrupt versus slow species turnover).

Keywords: Association analysis; Biclustering; Biplots; Cluster analysis; Correspondence analysis; Data visualisation; Dimension reduction; Finite mixture; Fuzzy clustering; Multidimensional scaling (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947313001862
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:71:y:2014:i:c:p:241-261

DOI: 10.1016/j.csda.2013.05.013

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().