A mixture of g-priors for variable selection when the number of regressors grows with the sample size

Minerva Mukhopadhyay () and Tapas Samanta
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Minerva Mukhopadhyay: Indian Statistical Institute
Tapas Samanta: Indian Statistical Institute

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2017, vol. 26, issue 2, No 7, 377-404

Abstract: Abstract We consider the problem of variable selection in linear regression using mixtures of g-priors. A number of mixtures have been proposed in the literature which work well, especially when the number of regressors p is fixed. In this paper, we propose a mixture of g-priors suitable for the case when p grows with the sample size n, more specifically when $$p=O(n^b)$$ p = O ( n b ) , $$0

Keywords: Model selection consistency; Misspecified models; General class of distributions of errors; Kullback–Leibler divergence; 62F15 Bayesian inference; 62F12 Asymptotic properties of estimators (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s11749-016-0516-0

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