bvpa: Bivariate Pareto Distribution
Implements the EM algorithm with one-step Gradient Descent method to estimate
the parameters of the Block-Basu bivariate Pareto distribution with location
and scale. We also found parametric bootstrap and asymptotic confidence
intervals based on the observed Fisher information of scale and shape parameters,
and exact confidence intervals for location parameters. Details are in
Biplab Paul and Arabin Kumar Dey (2023) <doi:10.48550/arXiv.1608.02199>
"An EM algorithm for absolutely continuous Marshall-Olkin bivariate Pareto
distribution with location and scale";
E L Lehmann and George Casella (1998) <doi:10.1007/b98854> "Theory of Point Estimation";
Bradley Efron and R J Tibshirani (1994) <doi:10.1201/9780429246593>
"An Introduction to the Bootstrap";
A P Dempster, N M Laird and D B Rubin
(1977) <www.jstor.org/stable/2984875> "Maximum Likelihood from Incomplete
Data via the EM Algorithm".
| Version: |
1.0.0 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
numDeriv, stats |
| Published: |
2023-08-08 |
| Author: |
Biplab Paul [aut, cre],
Arabin Kumar Dey [aut] |
| Maintainer: |
Biplab Paul <paul.biplab497 at gmail.com> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
no |
| CRAN checks: |
bvpa results |
Documentation:
Downloads:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=bvpa
to link to this page.