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MODEL SELECTION AND ESTIMATING DEGREES OF FREEDOM IN BAYESIAN LINEAR AND LINEAR MIXED EFFECT MODELS

Part of: Parametric inference Linear inference, regression Markov processes

Published online by Cambridge University Press:  13 November 2015

CHONG YOU
CHONG YOU*
Affiliation:
School of Mathematics and Statistics, University of Sydney, NSW 2006, Australia email cyou6879@uni.sydney.edu.au
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Abstract

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Keywords

consistencyMarkov chain Monte Carlodevianceinformation criterionresamplingbootstrapmean field variational Bayesspike and slab prior

MSC classification

Primary: 62J05: Linear regression
Secondary: 62F15: Bayesian inference 60J22: Computational methods in Markov chains 62F07: Ranking and selection
Type
Abstracts of Australasian PhD Theses
Information
Bulletin of the Australian Mathematical Society , Volume 93 , Issue 1 , February 2016 , pp. 164 - 166
Copyright
© 2015 Australian Mathematical Publishing Association Inc. 

References

Rue, H., Martino, S. and Chopin, N., ‘Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations’, J. R. Stat. Soc. B 71 (2009), 319–392.CrossRefGoogle Scholar
You, C., Ormerod, J. T. and Müller, S., ‘On variational Bayes estimation and variational information criteria for linear regression models’, Aust. N. Z. J. Stat. 56 (2014), 73–87.CrossRefGoogle Scholar
You, C., Müller, S. and Ormerod, J. T., ‘On generalized degrees of freedom with application in linear mixed models selection’, Stat. Comput. 25 (2015), 1–12.Google Scholar