Loss modeling with the size-biased lognormal mixture and the entropy regularized EM algorithm
From MaRDI portal
Publication:6573825
DOI10.1016/j.insmatheco.2024.05.003zbMath1545.91254MaRDI QIDQ6573825
Publication date: 17 July 2024
Published in: Insurance Mathematics \& Economics (Search for Journal in Brave)
lognormal distributionShannon entropyleft-truncated insurance lossesregularized EM algorithmsize-biased mixture
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- A Mathematical Theory of Communication
- On modeling left-truncated loss data using mixtures of distributions
- Statistical guarantees for the EM algorithm: from population to sample-based analysis
- Estimating conditional tail expectation with actuarial applications in view
- Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models
- Estimating the dimension of a model
- The distribution of the likelihood ratio for mixtures of densities from the one-parameter exponential family
- Modelling censored losses using splicing: a global fit strategy with mixed Erlang and extreme value distributions
- On the mixtures of length-biased Weibull distributions for loss severity modeling
- Mixture modeling of data with multiple partial right-censoring levels
- Modeling loss data using composite models
- Modeling loss data using mixtures of distributions
- Composite Lognormal–Pareto model with random threshold
- The Weibull–Pareto Composite Family with Applications to the Analysis of Unimodal Failure Rate Data
- Weighted Distributions and Size-Biased Sampling with Applications to Wildlife Populations and Human Families
- Practical Bayesian Density Estimation Using Mixtures of Normals
- Finite mixture models
- FITTING MIXTURES OF ERLANGS TO CENSORED AND TRUNCATED DATA USING THE EM ALGORITHM
- EFFICIENT ESTIMATION OF ERLANG MIXTURES USING iSCAD PENALTY WITH INSURANCE APPLICATION
- Modeling claims data with composite Stoppa models
- New composite models for the Danish fire insurance data
- Loss Models
- An Introduction to Heavy-Tailed and Subexponential Distributions
- Size-Biased Risk Measures of Compound Sums
- On the Class of Erlang Mixtures with Risk Theoretic Applications
- Using Model Averaging to Determine Suitable Risk Measure Estimates
- 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
- Ridge Regression: Biased Estimation for Nonorthogonal Problems
- Ridge Regression: Applications to Nonorthogonal Problems
- Assessing the performance of confidence intervals for high quantiles of Burr XII and Inverse Burr mixtures
This page was built for publication: Loss modeling with the size-biased lognormal mixture and the entropy regularized EM algorithm