Dissertation/Thesis Abstract

Robust Estimation of Parametric Models for Insurance Loss Data
by Poudyal, Chudamani, Ph.D., The University of Wisconsin - Milwaukee, 2018, 127; 10829498
Abstract (Summary)

Parametric statistical models for insurance claims severity are continuous, right-skewed, and frequently heavy-tailed. The data sets that such models are usually fitted to contain outliers that are difficult to identify and separate from genuine data. Moreover, due to commonly used actuarial “loss control strategies,” the random variables we observe and wish to model are affected by truncation (due to deductibles), censoring (due to policy limits), scaling (due to coinsurance proportions) and other transformations. In the current practice, statistical inference for loss models is almost exclusively likelihood (MLE) based, which typically results in non-robust parameter estimators, pricing models, and risk measures. To alleviate the lack of robustness of MLE-based inference in risk modeling, two broad classes of parameter estimators - Method of Trimmed Moments (MTM) and Method of Winsorized Moments (MWM) - have been recently developed. MTM and MWM estimators are sufficiently general and flexible, and possess excellent large- and small- sample properties, but they were designed for complete (not transformed) data. In this dissertation, we first redesign MTM estimators to be applicable to claim severity models that are fitted to truncated, censored, and insurance payments data. Asymptotic properties of such estimators are thoroughly investigated and their practical performance is illustrated using Norwegian fire claims data. In addition, we explore several extensions of MTM and MWM estimators for complete data. In particular, we introduce truncated, censored, and insurance payment-type estimators and study their asymptotic properties. Our analysis establishes new connections between data truncation, trimming, and censoring which paves the way for more effective modeling of non-linearly transformed loss data.

Indexing (document details)
Advisor: Brazauskas, Vytaras
Commitee: Beder, Jay H., Ghorai, Jugal K., Wade, Bruce, Wei, Wei
School: The University of Wisconsin - Milwaukee
Department: Mathematics
School Location: United States -- Wisconsin
Source: DAI-B 80/01(E), Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Mathematics, Statistics
Keywords: Insurance claim severity, Loss models, Parametric estimation, Robust statistics, Trimmed moments, Truncated moments
Publication Number: 10829498
ISBN: 978-0-438-36846-0
Copyright © 2019 ProQuest LLC. All rights reserved. Terms and Conditions Privacy Policy Cookie Policy
ProQuest