Efficient maximum likelihood estimation of copula based meta t-distributions

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Recently an efficient fixed point algorithm, called maximization by parts (MBP), for finding maximum likelihood estimates has been applied to models based on Gaussian copulas. It requires a decomposition of a likelihood function into two parts and their iterative maximization by solving score equations. For the first time, the MBP algorithm is applied to multivariate meta t-distributions based on t-copulas. Since score equations for meta t-distributions do not have closed forms the proposed MBP algorithm in two variations maximizes the decomposed parts of the likelihood iteratively. Superiority of the proposed MBP algorithm over standard estimation methods such as inference for margins and direct maximization is illustrated in a simulation study. The usefulness of the proposed algorithm is shown in two data applications.

Original languageEnglish
Pages (from-to)1196-1214
Number of pages19
JournalComputational Statistics and Data Analysis
Issue number3
StatePublished - 1 Mar 2011


  • Copula
  • Inference for margins
  • Maximization by parts
  • Maximum likelihood estimation
  • Meta-t distribution
  • Rolling windows


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