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

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 LICENSE
URL:	https://github.com/Roy-SR-007/bayesestdft
NeedsCompilation:	no
Materials:	README
CRAN checks:	bayesestdft results

Documentation:

Reference manual:

bayesestdft.pdf

Downloads:

Package source:	bayesestdft_1.0.0.tar.gz
Windows binaries:	r-devel: not available, r-release: not available, r-oldrel: not available
macOS binaries:	r-release (arm64): bayesestdft_1.0.0.tgz, r-oldrel (arm64): bayesestdft_1.0.0.tgz, r-release (x86_64): bayesestdft_1.0.0.tgz, r-oldrel (x86_64): bayesestdft_1.0.0.tgz

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