Joint modelling of the total amount and the number of claims by conditionals

Sarabia, José Marı́a; Guillén, Montserrat

doi:10.1016/j.insmatheco.2008.09.002

Cited by 8 publications

(3 citation statements)

References 27 publications

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“…Here, it should be noted that we do not consider dependence between the number of events and severity in terms of cost (Sarabia and Guillen, 2008). Further, a limitation of our study is that we only consider severe medical care events (namely, ambulance and emergency room use and hospitalization), whereas other medical treatments are not analysed and, as such, do not form a part of the insurance policy under evaluation.…”

Section: Discussionmentioning

confidence: 99%

Emergency care usage and longevity have opposite effects on health insurance rates

Piulachs¹,

Alemany

Guillén

2017

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To study the price of health insurance for individuals aged 65 years and over. Methodology: A sample of private health policyholders in Spain is analysed. Joint models are estimated for men and women, separately. A log-linear model of the transformed cumulated number of claims associated with emergency room occupation, ambulance use and hospitalization is estimated, together with a proportional hazard survival model. Findings: The association between the longitudinal process of severe medical care and the survival time process is positive and highly significant for both men and women. An increase in the price of health insurance due to the effect of a larger number of emergency care demand events is slightly offset by the decrease in expected longevity. Practical implications: The effect of an increase in the number of claims is small compared to the reduction in survival, so age still plays a central role in rate making. Originality: The proposed methodology allows dynamic rates to be designed, so that the price of health insurance can change as new usage information becomes available.

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Section: Discussionmentioning

confidence: 99%

Emergency care usage and longevity have opposite effects on health insurance rates

Piulachs¹,

Alemany

Guillén

2017

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“…This approach was not utilized in the current study to obtain covariances between quantitative and fitness traits in a more straightforward manner. Covariance between the two traits is generated based on the trivariate reduction method, which is a common method to generate covariance between two distributions that are non-normal (Sarabia & G omez-D eniz, 2008). For example, to generate a given correlation (q) between a Gamma 1 (shape 1 , scale 1 ) and Gamma 2 (shape 2 , scale 2 ), the following steps are taken:…”

Section: Step 1: Generating Historical Population and Genetic Archimentioning

confidence: 99%

Geno‐Diver: A combined coalescence and forward‐in‐time simulator for populations undergoing selection for complex traits

Howard

Tiezzi

Pryce

et al. 2017

J Animal Breeding Genetics

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Geno-Diver is a combined coalescence and forward-in-time simulator designed to simulate complex traits with a quantitative and/or fitness component and implement multiple selection and mating strategies utilizing pedigree or genomic information. The simulation is carried out in two steps. The first step generates whole-genome sequence data for founder individuals. A variety of trait architectures can be generated for quantitative and fitness traits along with their covariance. The second step generates new individuals forward-in-time based on a variety of selection and mating scenarios. Genetic values are predicted for individuals utilizing pedigree or genomic information. Relationship matrices and their associated inverses are generated using computationally efficient routines. We benchmarked Geno-Diver with a previous simulation program and described how to simulate a traditional quantitative trait along with a quantitative and fitness trait. A user manual with examples, source code in C++11 and executable versions of Geno-Diver for Linux are freely available at https://github.com/jeremyhoward/Geno-Diver.

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“…In this context, Sarabia and Guillén (2008) have considered extensions of the classical collective model assuming that the conditional distributions S|N and N|S belong to some prescribed parametric family, where S is the total claim amount and N is the number of claims. Gómez-Déniz and Calderín-Ojeda (2014), using conditional specification techniques, have obtained discrete distributions to be used in the collective risk model to compute the right-tail probability of the aggregate claims size distribution.…”

Section: Introductionmentioning

confidence: 99%