ΟΙΚΟΝΟΜΙΚΟ ΠΑΝΕΠΙΣΤΗΜΙΟ ΑΘΗΝΩΝ
ΤΜΗΜΑ ΣΤΑΤΙΣΤΙΚΗΣ
ΠΕΜΠΤΗ 10/4/2014
12:00 – 13:00
ΑΙΘΟΥΣΑ 607, 6ος ΟΡΟΦΟΣ,
ΚΤΙΡΙΟ ΜΕΤΑΠΤΥΧΙΑΚΩΝ ΣΠΟΥΔΩΝ
(ΕΥΕΛΠΙΔΩΝ & ΛΕΥΚΑΔΟΣ)
Erengul (Ozkok) Dodd
Faculty of Mathematical Sciences
University of Southampton
Modelling the time delay in Critical Illness Insurance using GB2 family of
distributions
ΠΕΡΙΛΗΨΗ (ΣΤΑ ΑΓΓΛΙΚΑ)
Critical Illness Insurance (CII) is subject to long delays between the diagnosis of a claim
and the corresponding settlement. Consideration of the uncertainty associated with different models
used in the estimation of the delay between diagnosis and settlement is crucial for financial
purposes, especially when the date of diagnosis is missing from the data records. Using UK data
supplied by the Continuous Mortality Investigation, we account for uncertainty in CII modelling by
exploring various claim delay distributions such as generalised beta of the second kind (GB2)
family of distributions in a generalised linear-type model setting. The non-recorded dates of
diagnosis are included in the analysis as missing values using their posterior predictive distribution.
The models are fitted under a hierarchical Bayesian framework using Markov chain Monte Carlo
methodology. Variable selection using Bayesian methodology to obtain the best model with
different prior distribution setups for the parameters is also discussed for a wide selection of error
distributions.
ATHENS UNIVERSITY OF ECONOMICS & BUSINESS
DEPARTMENT OF STATISTICS
Thursday 10/4/2014
12:00 – 13:00
ROOM 607, 6th FLOOR,
POSTGRADUATE STUDIES BUILDING
(EVELPIDON & LEFKADOS)
Erengul (Ozkok) Dodd
Faculty of Mathematical Sciences
University of Southampton
Modelling the time delay in Critical Illness Insurance using GB2 family of
distributions
ABSTRACT
Critical Illness Insurance (CII) is subject to long delays between the diagnosis of a claim
and the corresponding settlement. Consideration of the uncertainty associated with different models
used in the estimation of the delay between diagnosis and settlement is crucial for financial
purposes, especially when the date of diagnosis is missing from the data records. Using UK data
supplied by the Continuous Mortality Investigation, we account for uncertainty in CII modelling by
exploring various claim delay distributions such as generalised beta of the second kind (GB2)
family of distributions in a generalised linear-type model setting. The non-recorded dates of
diagnosis are included in the analysis as missing values using their posterior predictive distribution.
The models are fitted under a hierarchical Bayesian framework using Markov chain Monte Carlo
methodology. Variable selection using Bayesian methodology to obtain the best model with
different prior distribution setups for the parameters is also discussed for a wide selection of error
distributions.