## Bernoulli

• Bernoulli
• Volume 22, Number 4 (2016), 2080-2100.

### Consistency of Bayes factor for nonnested model selection when the model dimension grows

#### Abstract

Zellner’s $g$-prior is a popular prior choice for the model selection problems in the context of normal regression models. Wang and Sun [J. Statist. Plann. Inference 147 (2014) 95–105] recently adopt this prior and put a special hyper-prior for $g$, which results in a closed-form expression of Bayes factor for nested linear model comparisons. They have shown that under very general conditions, the Bayes factor is consistent when two competing models are of order $O(n^{\tau})$ for $\tau <1$ and for $\tau=1$ is almost consistent except a small inconsistency region around the null hypothesis. In this paper, we study Bayes factor consistency for nonnested linear models with a growing number of parameters. Some of the proposed results generalize the ones of the Bayes factor for the case of nested linear models. Specifically, we compare the asymptotic behaviors between the proposed Bayes factor and the intrinsic Bayes factor in the literature.

#### Article information

Source
Bernoulli, Volume 22, Number 4 (2016), 2080-2100.

Dates
First available in Project Euclid: 3 May 2016

https://projecteuclid.org/euclid.bj/1462297675

Digital Object Identifier
doi:10.3150/15-BEJ720

Mathematical Reviews number (MathSciNet)
MR3498023

Zentralblatt MATH identifier
1358.62033

#### Citation

Wang, Min; Maruyama, Yuzo. Consistency of Bayes factor for nonnested model selection when the model dimension grows. Bernoulli 22 (2016), no. 4, 2080--2100. doi:10.3150/15-BEJ720. https://projecteuclid.org/euclid.bj/1462297675

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