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

Research output: Contribution to journalArticlepeer-review

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

Fingerprint

Dive into the research topics of 'On the occasional exactness of the distributional transform approximation for direct Gaussian copula models with discrete margins'. Together they form a unique fingerprint.

Cite this