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


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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.

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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Working/discussion paper, preprint
Language English
Title Bayesian Estimation of the Degrees of Freedom Parameter of the Student-t Distribution---A Beneficial Re-parameterization
Year 2021
URL https://arxiv.org/abs/2109.01726v1

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Hosszejni, Darjus (Details)
Organization
Institute for Statistics and Mathematics IN (Details)
Research areas (ÖSTAT Classification 'Statistik Austria')
1162 Statistics (Details)
5323 Econometrics (Details)
5701 Applied statistics (Details)
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