Maximum likelihood inference for the multivariate t mixture model

Wan Lun Wang, Tsung I. Lin

研究成果: Article同行評審

10 引文 斯高帕斯(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.

頁(從 - 到)54-64
期刊Journal of Multivariate Analysis
出版狀態Published - 2016 7月 1

All Science Journal Classification (ASJC) codes

  • 統計與概率
  • 數值分析
  • 統計、概率和不確定性


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