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) <> "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>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: bvpa results


Reference manual: bvpa.pdf


Package source: bvpa_1.0.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): bvpa_1.0.0.tgz, r-oldrel (arm64): bvpa_1.0.0.tgz, r-release (x86_64): bvpa_1.0.0.tgz, r-oldrel (x86_64): bvpa_1.0.0.tgz


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