ROBUST AND EFFICIENT FITTING OF SEVERITY MODELS AND THE METHOD OF WINSORIZED MOMENTS

DOI10.1017/asb.2017.30zbMath1390.62230OpenAlexW2765226496MaRDI QIDQ5745195

J. K. Ghorai, Vytaras Brazauskas, Qian Zhao

Publication date: 5 June 2018

Published in: ASTIN Bulletin (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1017/asb.2017.30

zbMATH Keywords

robustnessefficiencyclaim severityinsurance layerwinsorized data

Mathematics Subject Classification ID

Related Items (7)

Premium rating without losses ⋮ Finite-sample performance of the T- and W-estimators for the Pareto tail index under data truncation and censoring ⋮ Maximum weighted likelihood estimator for robust heavy-tail modelling of finite mixture models ⋮ Computing and Estimating Distortion Risk Measures: How to Handle Analytically Intractable Cases? ⋮ Truncated, censored, and actuarial payment-type moments for robust fitting of a single-parameter Pareto distribution ⋮ ROBUST ESTIMATION OF LOSS MODELS FOR LOGNORMAL INSURANCE PAYMENT SEVERITY DATA ⋮ Small-sample performance of the MTM and MWM estimators for the parameters of log-location-scale families

Uses Software

• ROptEst
• QRM

Cites Work

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