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Estimating Correlations with Missing Data, a Bayesian Approach

Published online by Cambridge University Press:  01 January 2025

Alan L. Gross  and
Rocio Torres-Quevedo
Alan L. Gross*
Affiliation:
Graduate Center, City University of New York
Rocio Torres-Quevedo
Affiliation:
Graduate Center, City University of New York
*
Requests for reprints and copies of the FORTRAN computer program should be sent to Alan L. Gross, Department of Educational Psychology, Graduate Center, City University of New York, 33 West 42 Street, N.Y., N.Y. 10036.
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Abstract

The posterior distribution of the bivariate correlation is analytically derived given a data set where x is completely observed but y is missing at random for a portion of the sample. Interval estimates of the correlation are then constructed from the posterior distribution in terms of highest density regions (HDRs). Various choices for the form of the prior distribution are explored. For each of these priors, the resulting Bayesian HDRs are compared with each other and with intervals derived from maximum likelihood theory.

Keywords

missing dataBayesian statistics
Type
Original Paper
Information
Psychometrika , Volume 60 , Issue 3 , September 1995 , pp. 341 - 354
Copyright
Copyright © 1995 The Psychometric Society

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