Conjugate analysis of multivariate normal data with incomplete observations

Francesca Dominici, Giovanni Parmigiani, Merlise Clyde

Research output: Contribution to journalArticle

Abstract

The authors discuss prior distributions that are conjugate to the multivariate normal likelihood when some of the observations are incomplete. They present a general class of priors for incorporating information about unidentified parameters in the covariance matrix. They analyze the special case of monotone patterns of missing data, providing an explicit recursive form for the posterior distribution resulting from a conjugate prior distribution. They develop an importance sampling and a Gibbs sampling approach to sample from a general posterior distribution and compare the two methods.

Original languageEnglish (US)
Pages (from-to)533-550
Number of pages18
JournalCanadian Journal of Statistics
Volume28
Issue number3
Publication statusPublished - Sep 2000

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Keywords

  • Conjugate analysis
  • Data missing at random
  • Gibbs sampling
  • Importance sampling
  • Inverse Wishart distribution
  • Multivariate normal distribution

ASJC Scopus subject areas

  • Statistics and Probability

Cite this

Dominici, F., Parmigiani, G., & Clyde, M. (2000). Conjugate analysis of multivariate normal data with incomplete observations. Canadian Journal of Statistics, 28(3), 533-550.