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:

Reference manual: bvpa.pdf

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

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