Recombining dependent data: an Order Statistics approach

By Adolfo Alvarez and Daniel Peña in Research

December 1, 2009



This article discusses the problem of forming groups from previously split data. Algorithms for Cluster Analysis like SAR proposed by Peña, Rodriguez and Tiao (2004), divide the sample into small very homogeneous groups and then recombine them to form the definitive data configuration. This kind of splitting leads to dependent data in the sense that the groups are disjoint, so no traditional homogeneity of means or variances tests can be used.

We propose an alternative by using Order Statistics. Studying the distribution and some moments of linear combination of Order Statistics it is possible to recombine disjoint data groups when they merge into a sample from the same population.

Posted on:
December 1, 2009
1 minute read, 110 words
SAR Cluster Analysis Order Statistics L-statistics Bootstrapping
See Also:
Recombining observations in cluster analysis: the SAGRA method
Recombining partitions from multivariate data: a clustering method on Bayes factors