An EM algorithm for absolutely continuous Marshall-Olkin bivariate Pareto distribution with location and scale

TL;DR

This paper introduces an EM algorithm for estimating parameters of an absolutely continuous Block-Basu bivariate Pareto distribution with location and scale, derived from a Marshall-Olkin model, and demonstrates its application to rainfall data.

Contribution

The paper develops an EM algorithm for parameter estimation in a new absolutely continuous bivariate Pareto model with location and scale parameters, extending previous models.

Findings

The EM algorithm effectively estimates model parameters.

The model fits rainfall data for landslide risk assessment.

Simulation experiments validate the estimation method.

Abstract

In this paper, we have considered a Block-Basu type bivariate Pareto distribution. Here in the standard manner, first Marshall-Olkin type singular bivariate distribution has been constructed, and then by taking away the singular component similar to the Block and Basu model, an absolute continuous BB-BVPA model has been constructed. Further, the location and scale parameters also have been introduced. Therefore, the model has seven parameters. Different properties of this absolutely continuous distribution are derived. Since the maximum likelihood estimators of the parameters cannot be expressed in a closed form, we propose to use an EM algorithm to compute the estimators of the model parameters. Some simulation experiments have been performed for illustrative purposes. The model is fitted to rainfall data in the context of landslide risk estimation.