Hosszejni, Darjus. 2021. Bayesian Estimation of the Degrees of Freedom Parameter of the Student-t Distribution---A Beneficial Re-parameterization.

RIS

@MISC{Hosszejni2021,
title = {Bayesian Estimation of the Degrees of Freedom Parameter of the Student-t Distribution---A Beneficial Re-parameterization},
author = {Darjus Hosszejni},
year = {2021},
url = {https://arxiv.org/abs/2109.01726v1},
language = {EN},
abstract = {In this paper, conditional data augmentation (DA) is investigated for the degrees of freedom parameter ν of a Student-t distribution. Based on a restricted version of the expected augmented Fisher information, it is conjectured that the ancillarity DA is progressively more efficient for MCMC estimation than the sufficiency DA as ν increases; with the break even point lying at as low as ν≈4. The claim is examined further and generalized through a large simulation study and a application to U.S. macroeconomic time series. Finally, the ancillarity-sufficiency interweaving strategy is empirically shown to combine the benefits of both DAs. The proposed algorithm may set a new standard for estimating ν as part of any model.},
}