an efficient technique for bayesian modelling of family data using the bugs software

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ID: 168706
2014
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Abstract
Linear mixed models have become a popular tool to analyze continuous data from family-based designs by using random effects that model the correlation of subjects from the same family. However, mixed models for family data are challenging to implement with the BUGS (Bayesian inference Using Gibbs Sampling) software because of the high-dimensional covariance matrix of the random effects. This paper describes an efficient parameterization that utilizes the singular value decomposition of the covariance matrix of random effects, includes the BUGS code for such implementation, and extends the parameterization to generalized linear mixed models. The implementation is evaluated using simulated data and an example from a large family-based study is presented with a comparison to other existing methods.
Reference Key
bae2014frontiersan Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Harold T Bae;Thomas T Perls;Paola eSebastiani
Journal chemical record (new york, ny)
Year 2014
DOI
10.3389/fgene.2014.00390
URL
http://journal.frontiersin.org/Journal/10.3389/fgene.2014.00390/full
Keywords
covariance matrix

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