{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/bayesian-estimation-of-the-degrees-of-freedom","title":"Bayesian Estimation of the Degrees of Freedom Parameter of the Student-$t$ Distribution---A Beneficial Re-parameterization","arxiv_id":"2109.01726","date":"2021-09-03","proceeding":null,"authors":["Darjus Hosszejni"],"abstract":"In this paper, conditional data augmentation (DA) is investigated for the degrees of freedom parameter $\\nu$ 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 $\\nu$ increases; with the break even point lying at as low as $\\nu\\approx4$. 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 $\\nu$ as part of any model.","url_abs":"https://arxiv.org/abs/2109.01726v1","url_pdf":"https://arxiv.org/pdf/2109.01726v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"bayesian-estimation-of-the-degrees-of-freedom","repo_url":"https://github.com/hdarjus/student-t-asis-paper","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}