Automated Graduation using Bayesian Trans-dimensional Models

This paper presents a new method of graduation which uses parametric formulae together with Bayesian reversible jump Markov chain Monte Carlo methods. The aim is to provide a method which can be applied to a wide range of data, and which does not require a lot of adjustment or modification. The meth...

Full description

Saved in:
Bibliographic Details
Published in:Annals of actuarial science 2011-09, Vol.5 (2), p.231-251
Main Authors: Verrall, R.J., Haberman, S.
Format: Article
Language:English
Subjects:
Actuarial science
Algorithms
Automation
Bayesian analysis
Flexibility
International
Markov analysis
Measurement techniques
Methods
Mortality
Probability
Studies
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper presents a new method of graduation which uses parametric formulae together with Bayesian reversible jump Markov chain Monte Carlo methods. The aim is to provide a method which can be applied to a wide range of data, and which does not require a lot of adjustment or modification. The method also does not require one particular parametric formula to be selected: instead, the graduated values are a weighted average of the values from a range of formulae. In this way, the new method can be seen as an automatic graduation method which we believe can be applied in many cases without any adjustments and provide satisfactory graduated values. An advantage of a Bayesian approach is that it allows for model uncertainty unlike standard methods of graduation.
ISSN:1748-4995
1748-5002
DOI:10.1017/S1748499511000248