Cookies?
Library Header Image
LSE Research Online LSE Library Services

EM estimation for the Poisson-Inverse Gamma regression model with varying dispersion: an application to insurance ratemaking

Tzougas, George (2020) EM estimation for the Poisson-Inverse Gamma regression model with varying dispersion: an application to insurance ratemaking. Risks, 8 (3). pp. 1-23. ISSN 2227-9091

[img] Text (Tzougas_em-estimation-for-the-poisson-inverse-gamma--published) - Published Version
Available under License Creative Commons Attribution.

Download (689kB)

Identification Number: 10.3390/risks8030097

Abstract

This article presents the Poisson-Inverse Gamma regression model with varying dispersion for approximating heavy-tailed and overdispersed claim counts. Our main contribution is that we develop an Expectation-Maximization (EM) type algorithm for maximum likelihood (ML) estimation of the Poisson-Inverse Gamma regression model with varying dispersion. The empirical analysis examines a portfolio of motor insurance data in order to investigate the efficiency of the proposed algorithm. Finally, both the a priori and a posteriori, or Bonus-Malus, premium rates that are determined by the Poisson-Inverse Gamma model are compared to those that result from the classic Negative Binomial Type I and the Poisson-Inverse Gaussian distributions with regression structures for their mean and dispersion parameters.

Item Type: Article
Official URL: https://www.mdpi.com/journal/risks
Additional Information: © 2020 The Author
Divisions: Statistics
Subjects: H Social Sciences > HA Statistics
Date Deposited: 11 Sep 2020 11:30
Last Modified: 20 Sep 2021 02:18
URI: http://eprints.lse.ac.uk/id/eprint/106539

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics