Robust Estimation of Loss Models for Truncated and Censored Severity Data

Autor:	Poudyal, Chudamani, Brazauskas, Vytaras
Rok vydání:	2022
Předmět:	Statistics - Methodology Mathematics - Statistics Theory Statistics - Computation
Druh dokumentu:	Working Paper
Popis:	In this paper, we consider robust estimation of claim severity models in insurance, when data are affected by truncation (due to deductibles), censoring (due to policy limits), and scaling (due to coinsurance). In particular, robust estimators based on the methods of trimmed moments (T-estimators) and winsorized moments (W-estimators) are pursued and fully developed. The general definitions of such estimators are formulated and their asymptotic properties are investigated. For illustrative purposes, specific formulas for T- and W-estimators of the tail parameter of a single-parameter Pareto distribution are derived. The practical performance of these estimators is then explored using the well-known Norwegian fire claims data. Our results demonstrate that T- and W-estimators offer a robust and computationally efficient alternative to the likelihood-based inference for models that are affected by deductibles, policy limits, and coinsurance. Comment: 32 pages, 2 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2202.13000 Zobrazit plný text záznamu View this record from Arxiv