首页|Consistency of objective Bayes factors for nonnested linear models and increasing model dimension
Consistency of objective Bayes factors for nonnested linear models and increasing model dimension
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
Casella et al. [2, (2009)] proved that, under very general conditions, for normal linear models the Bayes factor for a wide class of prior distributions, including the intrinsic priors, is consistent when the number of parameters does not grow with the sample size n. The special attention paid to the intrinsic priors is due to the fact that they are nonsubjective priors, and thus accessible priors for complex models.The case where the number of parameters of nested models grows as O (n~α) for α ≤ 1 was considered in Moreno et al. [13, (2010)], in which it was proved that the Bayes factor for intrinsic priors is consistent for the case where both models are of order O(n~α) for a < 1, and for a = 1 is consistent except for a small set of alternative models. The small set of models for which consistency does not hold was characterized in terms of a pseudo-distance between models.The goal of the present article is to extend the above results to the case where the linear models are nonnested. As the comparison of nonnested models calls for a method of encompassing, for proving consistency we use encompassing from below in this paper.
asymptoticsBICintrinsic priorslinear modelsmultiplicity of parametersrate of growth
F. Javier Giron、Elias Moreno、George Casella、M. L. Martinez
展开 >
Department of Statistics, University of Malaga, 29071 Malaga Spain
Department of Statistics, University of Granada, 18071, Granada, Spain
Department of Statistics, University of Florida, Gainesville, FL 32611 USA
NETL
NSTL
