The Use of a Log-Normal Prior for the Student t-Distribution

It is typically difficult to estimate the number of degrees of freedom due to the leptokurtic nature of the Student t-distribution. Particularly in studies with small sample sizes, special care is needed concerning prior choice in order to ensure that the analysis is not overly dominated by any prior distribution. In this article, popular priors used in the existing literature are examined by characterizing their distributional properties on an effective support where it is desirable to concentrate on most of the prior probability mass. Additionally, we suggest a log-normal prior as a viable prior option. We show that the Bayesian estimator based on a log-normal prior compares favorably to other Bayesian estimators based on the priors previously proposed via simulation studies and financial applications.

Automated summary

Estimating the number of degrees of freedom is challenging due to the leptokurtic nature of the Student t-distribution, especially in studies with small sample sizes. This article examines popular priors used in the literature and proposes a log-normal prior as a viable option, demonstrating its superiority through simulation studies and financial applications.