[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.5351/CSAM.2013.20.4.311

A Robust Estimation for the Composite Lognormal-Pareto Model

Pak, Ro Jin (Department of Applied Statistics, Dankook University)

Publication Information

Communications for Statistical Applications and Methods / v.20, no.4, 2013 , pp. 311-319 More about this Journal

Abstract

Cooray and Ananda (2005) proposed a composite lognormal-Pareto model to analyze loss payment data in the actuarial and insurance industries. Their model is based on a lognormal density up to an unknown threshold value and a two-parameter Pareto density. In this paper, we implement the minimum density power divergence estimation for the composite lognormal-Pareto density. We compare the performances of the minimum density power divergence estimator (MDPDE) and the maximum likelihood estimator (MLE) by simulations and an example. The minimum density power divergence estimator performs reasonably well against various violations in the distribution. The minimum density power divergence estimator better fits small observations and better resists against extraordinary large observations than the maximum likelihood estimator.

Keywords

Lognormal distribution; maximum likelihood estimation; minimum distance estimation; Pareto distribution;

Citations & Related Records

Reference

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