mvClaim : an R package for multivariate general insurance claims severity modelling

2021 ◽  
pp. 1-17
Author(s):  
Sen Hu ◽  
T. Brendan Murphy ◽  
Adrian O’Hagan
Keyword(s):  
R Package ◽  
Finite Mixture ◽  
Current Version ◽  
Mixture Of Experts ◽  
Insurance Claim ◽  
General Insurance ◽  
Gamma Distributions ◽  
Approach Theory ◽  
Modelling Approach ◽  
Model Family

Abstract The mvClaim package in R provides flexible modelling frameworks for multivariate insurance claim severity modelling. The current version of the package implements a parsimonious mixture of experts (MoE) model family with bivariate gamma distributions, as introduced in Hu et al., and a finite mixture of copula regressions within the MoE framework as in Hu & O’Hagan. This paper presents the modelling approach theory briefly and the usage of the models in the package in detail. This package is hosted on GitHub at https://github.com/senhu/.

LRMoE: An R Package for Flexible Actuarial Loss Modelling Using Mixture of Experts Regression Model

2020 ◽  
Author(s):  
Spark C. Tseung ◽  
Andrei Badescu ◽  
Tsz Chai Fung ◽  
Xiaodong Sheldon Lin
Keyword(s):  
Regression Model ◽  
R Package ◽  
Mixture Of Experts ◽  
Loss Modelling

scholarly journals Modelling Unobserved Heterogeneity in Claim Counts Using Finite Mixture Models

Risks ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 10 ◽  
Author(s):  
Lluís Bermúdez ◽  
Dimitris Karlis ◽  
Isabel Morillo
Keyword(s):  
Mixture Models ◽  
Finite Mixture Models ◽  
Unobserved Heterogeneity ◽  
Finite Mixture ◽  
Insurance Claim ◽  
Poisson Models ◽  
Excess Of Zeros ◽  
Car Insurance ◽  
Insurance Data ◽  
Natural Way

When modelling insurance claim count data, the actuary often observes overdispersion and an excess of zeros that may be caused by unobserved heterogeneity. A common approach to accounting for overdispersion is to consider models with some overdispersed distribution as opposed to Poisson models. Zero-inflated, hurdle and compound frequency models are typically applied to insurance data to account for such a feature of the data. However, a natural way to deal with unobserved heterogeneity is to consider mixtures of a simpler models. In this paper, we consider k-finite mixtures of some typical regression models. This approach has interesting features: first, it allows for overdispersion and the zero-inflated model represents a special case, and second, it allows for an elegant interpretation based on the typical clustering application of finite mixture models. k-finite mixture models are applied to a car insurance claim dataset in order to analyse whether the problem of unobserved heterogeneity requires a richer structure for risk classification. Our results show that the data consist of two subpopulations for which the regression structure is different.

scholarly journals Recent Enhancements to the Genetic Risk Prediction Model BRCAPRO

2015 ◽  
Vol 14s2 ◽  
pp. CIN.S17292 ◽  
Author(s):  
Emanuele Mazzola ◽  
Amanda Blackford ◽  
Giovanni Parmigiani ◽  
Swati Biswas
Keyword(s):  
Risk Prediction ◽  
Genetic Risk ◽  
Deleterious Mutation ◽  
Time Window ◽  
R Package ◽  
Current Version ◽  
Breast And Ovarian Cancer ◽  
Genetic Risk Prediction ◽  
History Of ◽  
Available Information

BRCAPRO is a widely used model for genetic risk prediction of breast cancer. It is a function within the R package BayesMendel and is used to calculate the probabilities of being a carrier of a deleterious mutation in one or both of the BRCA genes, as well as the probability of being affected with breast and ovarian cancer within a defined time window. Both predictions are based on information contained in the counselee's family history of cancer. During the last decade, BRCAPRO has undergone several rounds of successive refinements: the current version is part of release 2.1 of BayesMendel. In this review, we showcase some of the most notable features of the software resulting from these recent changes. We provide examples highlighting each feature, using artificial pedigrees motivated by complex clinical examples. We illustrate how BRCAPRO is a comprehensive software for genetic risk prediction with many useful features that allow users the flexibility to incorporate varying amounts of available information.

LiDAR and Weibull modeling of diameter and basal area

2008 ◽  
Vol 84 (6) ◽  
pp. 866-875 ◽  
Author(s):  
V. Thomas ◽  
R D Oliver ◽  
K. Lim ◽  
M. Woods
Keyword(s):  
Finite Mixture Models ◽  
Basal Area ◽  
Weibull Model ◽  
Mixture Modeling ◽  
Finite Mixture ◽  
Stem Density ◽  
Promising Technique ◽  
Diameter Class ◽  
Modelling Approach ◽  
Diameter Distributions

This study investigates the ability to predict forest diameter distributions from light detection and ranging (LiDAR) data using Weibull modelling for forest stands in central Ontario. Results suggest that the unimodal 2-parameter Weibull model is a promising technique for the prediction of diameter class distributions, with strong relationships evident for several subgroups (at 95% confidence, r2adj=0.83, 0.78, 0.88, 0.80, 0.83, and 0.65, with validation RMSE of 4.09 m2/ha, 0.61 stems/ha, 6.05, 0.64, 4.73, and 0.09 for basal area, stem density, and the Weibull a and b parameters for basal area and stem density, respectively). The unimodal models were found to be least effective for the irregularly shaped diameter distributions, particularly for low-density coniferous plots that have undergone shelterwood treatment. A significant improvement in results for these irregular plots was found with a finite mixture modelling approach, suggesting that finite mixture models may extend our ability to predict diameter distributions over large portions of the landscape. Key words: LiDAR, Weibull, finite mixture modeling, diameter class distributions, multiple linear regression

Finite mixture of gamma distributions: A conjugate prior

2007 ◽  
Vol 51 (9) ◽  
pp. 4369-4378 ◽  
Author(s):  
Jamal A. Al-Saleh ◽  
Satish K. Agarwal
Keyword(s):  
Finite Mixture ◽  
Conjugate Prior ◽  
Gamma Distributions

scholarly journals Agents, Bayes, and Climatic Risks - a modular modelling approach

2005 ◽  
Vol 4 ◽  
pp. 3-7 ◽  
Author(s):  
A. Haas ◽  
C. Jaeger
Keyword(s):  
Operating System ◽  
Social Agents ◽  
Climate Risks ◽  
Parallel Processes ◽  
Insurance Firms ◽  
Climatic Risks ◽  
Energy Companies ◽  
Modular Modelling ◽  
Modelling Approach ◽  
Model Family

Abstract. When insurance firms, energy companies, governments, NGOs, and other agents strive to manage climatic risks, it is by no way clear what the aggregate outcome should and will be. As a framework for investigating this subject, we present the LAGOM model family. It is based on modules depicting learning social agents. For managing climate risks, our agents use second order probabilities and update them by means of a Bayesian mechanism while differing in priors and risk aversion. The interactions between these modules and the aggregate outcomes of their actions are implemented using further modules. The software system is implemented as a series of parallel processes using the CIAMn approach. It is possible to couple modules irrespective of the language they are written in, the operating system under which they are run, and the physical location of the machine.

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