ROBUST ESTIMATION OF LOSS MODELS FOR LOGNORMAL INSURANCE PAYMENT SEVERITY DATA
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Publication:5152546
DOI10.1017/asb.2021.4zbMath1479.91339arXiv2103.02089OpenAlexW3135265667MaRDI QIDQ5152546
Publication date: 24 September 2021
Published in: ASTIN Bulletin (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2103.02089
dynamic estimationmaximum likelihood estimatorsrobust estimationtruncated and censored datatrimmed momentsloss modelsinsurance paymentslognormal insurance severity
Applications of statistics to actuarial sciences and financial mathematics (62P05) Actuarial mathematics (91G05)
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Cites Work
- Unnamed Item
- On modeling left-truncated loss data using mixtures of distributions
- Robust fitting of claim severity distributions and the method of trimmed moments
- Compound unimodal distributions for insurance losses
- Modelling censored losses using splicing: a global fit strategy with mixed Erlang and extreme value distributions
- Mixture modeling of data with multiple partial right-censoring levels
- Fitting multivariate Erlang mixtures to data: a roughness penalty approach
- Truncated, censored, and actuarial payment-type moments for robust fitting of a single-parameter Pareto distribution
- Modeling loss data using composite models
- Modeling loss data using mixtures of distributions
- Estimation of parameters of doubly truncated normal distribution from first four sample moments
- On estimating the mean and variance of singly truncated normal frequency distributions from the first three sample moments
- Approximation Theorems of Mathematical Statistics
- The Influence Curve and Its Role in Robust Estimation
- Asymptotic Statistics
- MODELLING INSURANCE LOSSES USING CONTAMINATED GENERALISED BETA TYPE-II DISTRIBUTION
- FITTING MIXTURES OF ERLANGS TO CENSORED AND TRUNCATED DATA USING THE EM ALGORITHM
- Loss Models
- Modeling with Weibull-Pareto Models
- Extending composite loss models using a general framework of advanced computational tools
- Modeling Severity and Measuring Tail Risk of Norwegian Fire Claims
- Modeling actuarial data with a composite lognormal-Pareto model
- On composite lognormal-Pareto models
- Robust Estimation of a Location Parameter
- Asymptotic Distribution of Linear Combinations of Functions of Order Statistics with Applications to Estimation
- Efficient and Robust Fitting of Lognormal Distributions
- Robust and Efficient Estimation of the Tail Index of a Single-Parameter Pareto Distribution
- Understanding Relationships Using Copulas
- ROBUST AND EFFICIENT FITTING OF SEVERITY MODELS AND THE METHOD OF WINSORIZED MOMENTS
- Estimating the Mean and Variance of Normal Populations from Singly Truncated and Doubly Truncated Samples
- On Some Functions Involving Mill's Ratio
- Robust Statistics
