Maximum likelihood inference for the multivariate t mixture model

Wan Lun Wang, Tsung I. Lin

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


Multivariate t mixture (TMIX) models have emerged as a powerful tool for robust modeling and clustering of heterogeneous continuous multivariate data with observations containing longer than normal tails or atypical observations. In this paper, we explicitly derive the score vector and Hessian matrix of TMIX models to approximate the information matrix under the general and three special cases. As a result, the standard errors of maximum likelihood (ML) estimators are calculated using the outer-score, Hessian matrix, and sandwich-type methods. We have also established some asymptotic properties under certain regularity conditions. The utility of the new theory is illustrated with the analysis of real and simulated data sets.

Original languageEnglish
Pages (from-to)54-64
Number of pages11
JournalJournal of Multivariate Analysis
Publication statusPublished - 2016 Jul 1

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Numerical Analysis
  • Statistics, Probability and Uncertainty


Dive into the research topics of 'Maximum likelihood inference for the multivariate t mixture model'. Together they form a unique fingerprint.

Cite this