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The multivariate normal model

  • Peter D. Hoff
Chapter
Part of the Springer Texts in Statistics book series (STS)

Abstract

Up until now all of our statistical models have been univariate models, that is, models for a single measurement on each member of a sample of individuals or each run of a repeated experiment. However, datasets are frequently multivariate, having multiple measurements for each individual or experiment. This chapter covers what is perhaps the most useful model for multivariate data, the multivariate normal model, which allows us to jointly estimate population means, variances and correlations of a collection of variables. After first calculating posterior distributions under semiconjugate prior distributions, we show how the multivariate normal model can be used to impute data that are missing at random.

Keywords

Covariance Matrix Posterior Distribution Prior Distribution Reading Comprehension Multivariate Normal Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Department of StatisticsUniversity of WashingtonSeattleUSA

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