doi:10.3390/axioms11090462> to sample from the posterior distribution of the degrees of freedom. A random walk Metropolis algorithm is used for sampling when Jeffrey's and Gamma priors are endowed upon the degrees of freedom. In addition, the Metropolis-adjusted Langevin algorithm for sampling is used under the Jeffrey's prior specification. The Log-normal prior over the degrees of freedom is posed as a viable choice with comparable performance in simulations and real-data application, against other prior choices, where an Elliptical Slice Sampler is used to sample from the concerned posterior.">

bayesestdft: Estimating the Degrees of Freedom of the Student's t-Distribution under a Bayesian Framework (original) (raw)

A Bayesian framework to estimate the Student's t-distribution's degrees of freedom is developed. Markov Chain Monte Carlo sampling routines are developed as in <doi:10.3390/axioms11090462> to sample from the posterior distribution of the degrees of freedom. A random walk Metropolis algorithm is used for sampling when Jeffrey's and Gamma priors are endowed upon the degrees of freedom. In addition, the Metropolis-adjusted Langevin algorithm for sampling is used under the Jeffrey's prior specification. The Log-normal prior over the degrees of freedom is posed as a viable choice with comparable performance in simulations and real-data application, against other prior choices, where an Elliptical Slice Sampler is used to sample from the concerned posterior.

Version: 1.0.0
Depends: R (≥ 4.0.4)
Imports: numDeriv, dplyr
Published: 2025-01-09
DOI: 10.32614/CRAN.package.bayesestdft
Author: Somjit Roy [aut, cre], Se Yoon Lee [aut, ctb]
Maintainer: Somjit Roy <sroy_123 at tamu.edu>
BugReports: https://github.com/Roy-SR-007/bayesestdft/issues
License: MIT + file
URL: https://github.com/Roy-SR-007/bayesestdft
NeedsCompilation: no
Materials: README
CRAN checks: bayesestdft results

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