Clustering dependent observations with copula functions

This paper deals with the problem of clustering dependent observations according to their underlying complex generating process. Di Lascio and Giannerini (Journal of Classification 29(1):50–75, 2012 ) introduced the CoClust, a clustering algorithm based on copula function that achieves the task but...

Full description

Saved in:
Bibliographic Details
Published in:Statistical papers (Berlin, Germany) Germany), 2019-02, Vol.60 (1), p.35-51
Main Authors: Di Lascio, F. Marta L., Giannerini, Simone
Format: Article
Language:English
Subjects:
Algorithms
Breast cancer
Clustering
Economic Theory/Quantitative Economics/Mathematical Methods
Economics
Finance
Insurance
Management
Mathematics and Statistics
Operations Research/Decision Theory
Probability Theory and Stochastic Processes
Regular Article
Statistics
Statistics for Business
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper deals with the problem of clustering dependent observations according to their underlying complex generating process. Di Lascio and Giannerini (Journal of Classification 29(1):50–75, 2012 ) introduced the CoClust, a clustering algorithm based on copula function that achieves the task but has a high computational burden. Moreover, the CoClust automatically allocates all the observations to the clusters; thus, it cannot discard potentially irrelevant observations. In this paper we introduce an improved version of the CoClust that both overcomes these issues and performs better in many respects. By means of a Monte Carlo study we investigate the features of the algorithm and show that it improves consistently with respect to the old CoClust. The validity of our proposal is also supported by applications to real data sets of human breast tumor samples for which the algorithm provides a meaningful biological interpretation. The new algorithm is implemented and made available through an updated version of the R package CoClust.
ISSN:0932-5026
1613-9798
DOI:10.1007/s00362-016-0822-3