Penalized cluster analysis with applications to family data

Yixin Fang, Junhui Wang

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

The goal of cluster analysis is to assign observations into clusters so that observations in the same cluster are similar in some sense. Many clustering methods have been developed in the statistical literature, but these methods are inappropriate for clustering family data, which possess intrinsic familial structure. To incorporate the familial structure, we propose a form of penalized cluster analysis with a tuning parameter controlling the tradeoff between the observation dissimilarity and the familial structure. The tuning parameter is selected based on the concept of clustering stability. The effectiveness of the method is illustrated via simulations and an application to a family study of asthma.

Original languageEnglish (US)
Pages (from-to)2128-2136
Number of pages9
JournalComputational Statistics and Data Analysis
Volume55
Issue number6
DOIs
StatePublished - Jun 1 2011

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

Keywords

  • Consistency
  • Cross-validation
  • K-means
  • Kinship
  • Stability

Fingerprint Dive into the research topics of 'Penalized cluster analysis with applications to family data'. Together they form a unique fingerprint.

Cite this