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 |
Documentation:
Downloads:
Linking:
Please use the canonical formhttps://CRAN.R-project.org/package=bayesestdftto link to this page.