A Bayesian solution to reconstructing centrally censored distributions

Baker, Peter, Mengersen, Kerrie and Davis, Gerard (2005) A Bayesian solution to reconstructing centrally censored distributions. Journal of Agricultural, Biological, and Environmental Statistics, 10 1: 61-83. doi:10.1198/108571105X28697

Author Baker, Peter
Mengersen, Kerrie
Davis, Gerard
Title A Bayesian solution to reconstructing centrally censored distributions
Journal name Journal of Agricultural, Biological, and Environmental Statistics   Check publisher's open access policy
ISSN 1085-7117
Publication date 2005-03-01
Sub-type Article (original research)
DOI 10.1198/108571105X28697
Volume 10
Issue 1
Start page 61
End page 83
Total pages 23
Place of publication Alexandria, VA, U.S.
Publisher American Statistical Association
Language eng
Subject 06 Biological Sciences
0601 Biochemistry and Cell Biology
Abstract Bayesian methods are investigated for the reconstruction of mixtures in the case of central censoring. Earlier literature suggested that when the relationship between a continuous and a categorical variable is of interest, a cost-efficient strategy may be to measure the categorical variable only in the tails of the continuous distribution. Such samples occur in population epidemiology and gene mapping. Because central observations are not classified, the mixture component to which each observation belongs is not known. Three cases of censoring, which correspond to differing amounts of available information, are compared. Closed form solutions are not available and so Markov chain Monte Carlo techniques are employed to estimate posterior densities. Evidence for a mixture of two populations is assessed via Bayes factors calculated using a Laplace–Metropolis estimator. Although parameter estimates appear to be satisfactory in most situations, evidence of two populations is only found when the component populations are well separated, tail sizes are not too small, or typing information is available. Extension of these methods to incorporate fixed effects is illustrated by application to a cattle breeding experiment.
Keyword Cattle breeding experiment
Monte Carlo Method
Bayesian Methods
Gene Mapping
Population Epidemiology
Markov chain Monte Carlo methods
Posterior densities
Laplace -Metropolis estimator
Q-Index Code C1

Document type: Journal Article
Sub-type: Article (original research)
Collections: Excellence in Research Australia (ERA) - Collection
School of Public Health Publications
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Created: Tue, 22 Dec 2009, 13:49:18 EST by Rosalind Blair on behalf of Faculty Of Health Sciences