On the occasional exactness of the distributional transform approximation for direct Gaussian copula models with discrete margins

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Abstract

The direct Gaussian copula model with discrete margins is appealing but poses computational challenges due to its intractable likelihood. We show that the distributional transform-based approximate likelihood is essentially exact for some variants of the model, and we propose a quantity that can be used to assess exactness for a given dataset.

Original languageEnglish (US)
Article number109159
JournalStatistics and Probability Letters
Volume177
DOIs
StatePublished - Oct 2021

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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