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.
|Number of pages||22|
|Journal||Journal of the Royal Statistical Society Series C: Applied Statistics|
|Early online date||25 Jun 2016|
|Publication status||Published - Feb 2017|