Identification and forecasting in mortality models

Nielsen, B. & Nielsen, J. P. (2014). Identification and forecasting in mortality models. The Scientific World Journal, 2014, 347043 - ?. doi: 10.1155/2014/347043

[img]
Preview
PDF - Published Version
Available under License Creative Commons: Attribution-Noncommercial 3.0.

Download (957kB) | Preview

Abstract

Mortality models often have inbuilt identification issues challenging the statistician. The statistician can choose to work with well-defined freely varying parameters, derived as maximal invariants in this paper, or with ad hoc identified parameters which at first glance seem more intuitive, but which can introduce a number of unnecessary challenges. In this paper we describe the methodological advantages from using the maximal invariant parameterisation and we go through the extra methodological challenges a statistician has to deal with when insisting on working with ad hoc identifications. These challenges are broadly similar in frequentist and in Bayesian setups. We also go through a number of examples from the literature where ad hoc identifications have been preferred in the statistical analyses.

Item Type: Article
Subjects: H Social Sciences > H Social Sciences (General)
Q Science > QA Mathematics
R Medicine > RA Public aspects of medicine
Divisions: Cass Business School > Faculty of Actuarial Science & Insurance
Related URLs:
URI: http://openaccess.city.ac.uk/id/eprint/4638

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics