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Prediction of settlement delay in critical illness insurance claims using GB2 distribution

Prediction of settlement delay in critical illness insurance claims using GB2 distribution
Prediction of settlement delay in critical illness insurance claims using GB2 distribution
We analyse the delay between diagnosis of illness and claim settlement in critical illness insurance by using generalized linear‐type models under a generalized beta of the second kind family of distributions. A Bayesian approach is employed which allows us to incorporate parameter and model uncertainty and also to impute missing data in a natural manner. We propose methodology involving a latent likelihood ratio test to compare missing data models and a version of posterior predictive p‐values to assess different models. Bayesian variable selection is also performed, supporting a small number of models with small Bayes factors, and therefore we base our predictions on model averaging instead of on a best‐fitting model.
0035-9254
273-294
Dodd, Erengul
b3faed76-f22b-4928-a922-7f0b8439030d
Streftaris, George
c183b8ea-cf3f-4f5c-8266-b4486b458792
Dodd, Erengul
b3faed76-f22b-4928-a922-7f0b8439030d
Streftaris, George
c183b8ea-cf3f-4f5c-8266-b4486b458792

Dodd, Erengul and Streftaris, George (2017) Prediction of settlement delay in critical illness insurance claims using GB2 distribution. Journal of the Royal Statistical Society, Series C (Applied Statistics), 66 (2), 273-294. (doi:10.1111/rssc.12165).

Record type: Article

Abstract

We analyse the delay between diagnosis of illness and claim settlement in critical illness insurance by using generalized linear‐type models under a generalized beta of the second kind family of distributions. A Bayesian approach is employed which allows us to incorporate parameter and model uncertainty and also to impute missing data in a natural manner. We propose methodology involving a latent likelihood ratio test to compare missing data models and a version of posterior predictive p‐values to assess different models. Bayesian variable selection is also performed, supporting a small number of models with small Bayes factors, and therefore we base our predictions on model averaging instead of on a best‐fitting model.

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More information

Submitted date: 25 September 2015
Accepted/In Press date: 28 April 2016
e-pub ahead of print date: 25 June 2016
Published date: February 2017
Organisations: Statistics

Identifiers

Local EPrints ID: 391350
URI: http://eprints.soton.ac.uk/id/eprint/391350
ISSN: 0035-9254
PURE UUID: 5cc4a897-cf8b-48ce-ae8e-897c56cb4902
ORCID for Erengul Dodd: ORCID iD orcid.org/0000-0001-6658-0990

Catalogue record

Date deposited: 19 Apr 2016 13:26
Last modified: 10 Dec 2019 06:35

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