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 language | English (US) |
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Pages (from-to) | 533-550 |
Number of pages | 18 |
Journal | Canadian Journal of Statistics |
Volume | 28 |
Issue number | 3 |
State | Published - Sep 2000 |
Keywords
- Conjugate analysis
- Data missing at random
- Gibbs sampling
- Importance sampling
- Inverse Wishart distribution
- Multivariate normal distribution
ASJC Scopus subject areas
- Statistics and Probability