Dichotomous unimodal compound models: application to the distribution of insurance losses

DOI10.1080/02664763.2020.1789076OpenAlexW3040917674MaRDI QIDQ5861418

Salvatore D. Tomarchio, Antonio Punzo

Publication date: 1 March 2022

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02664763.2020.1789076

zbMATH Keywords

log-normal distributionvalue at risktail value at riskinsurance lossescompound modelunimodal gamma distribution

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to actuarial sciences and financial mathematics (62P05) Parametric inference (62F99) Applications of statistics (62Pxx)

Related Items

Outlier detection in multivariate functional data through a contaminated mixture model ⋮ Modeling the cryptocurrency return distribution via Laplace scale mixtures ⋮ Mixtures of Matrix-Variate Contaminated Normal Distributions ⋮ Modelling students’ career indicators via mixtures of parsimonious matrix‐normal distributions ⋮ Parsimonious mixtures for the analysis of tensor-variate data ⋮ The exponential T-X family of distributions: properties and an application to insurance data ⋮ Analysis of skewed data by using compound Poisson exponential distribution with applications to insurance claims ⋮ Univariate and bivariate compound models based on random sum of variates with application to the insurance losses data ⋮ Editorial to special issue V WCDANM 2018

Uses Software

• R
• MASS (R)
• RootSolve
• CASdatasets
• fGarch
• HyperbolicDist
• Rugarch
• fitdistrplus
• glogis

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• A simple generalisation of the Hill estimator
• Decision boundaries for mixtures of regressions
• On the favorable estimation for fitting heavy tailed data
• On a multivariate gamma distribution
• Estimating the dimension of a model
• Compound unimodal distributions for insurance losses
• A gamma kernel density estimation for insurance loss data
• Unconstrained representation of orthogonal matrices with application to common principal components
• Mixtures of multivariate contaminated normal regression models
• Modeling loss data using composite models
• Robust clustering in regression analysis via the contaminated Gaussian cluster-weighted model
• A new class of models for heavy tailed distributions in finance and insurance risk
• Skew mixture models for loss distributions: a Bayesian approach
• Parsimonious mixtures of multivariate contaminated normal distributions
• Folded and log-folded-tdistributions as models for insurance loss data
• Composite Lognormal–Pareto model with random threshold
• The Identification of Multiple Outliers
• Statistical Analysis of Financial Data in S-Plus
• Mixture Models, Outliers, and the EM Algorithm
• Modelling of extremal events in insurance and finance
• Wild adaptive trimming for robust estimation and cluster analysis
• Numerical Maximisation of Likelihood: A Neglected Alternative to EM?
• Modeling actuarial data with a composite lognormal-Pareto model
• Hidden Markov Models for Time Series
• Probability density function estimation using gamma kernels
• A new look at the statistical model identification