Identification and Forecasting in Mortality Models

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 wh...

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Main Authors: Bent Nielsen, Jens P. Nielsen
Format: Article
Language:English
Published: Hindawi Limited 2014-01-01
Series:The Scientific World Journal
Online Access:http://dx.doi.org/10.1155/2014/347043
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spelling doaj-808baf9932cc4797911a187c90d81e062020-11-24T21:28:55ZengHindawi LimitedThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/347043347043Identification and Forecasting in Mortality ModelsBent Nielsen0Jens P. Nielsen1Department of Economics, University of Oxford, Oxford OX1 2JD, UKCass Business School, City University London, 106 Bunhill Row, London EC1Y 8TZ, UKMortality 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.http://dx.doi.org/10.1155/2014/347043
collection DOAJ
language English
format Article
sources DOAJ
author Bent Nielsen
Jens P. Nielsen
spellingShingle Bent Nielsen
Jens P. Nielsen
Identification and Forecasting in Mortality Models
The Scientific World Journal
author_facet Bent Nielsen
Jens P. Nielsen
author_sort Bent Nielsen
title Identification and Forecasting in Mortality Models
title_short Identification and Forecasting in Mortality Models
title_full Identification and Forecasting in Mortality Models
title_fullStr Identification and Forecasting in Mortality Models
title_full_unstemmed Identification and Forecasting in Mortality Models
title_sort identification and forecasting in mortality models
publisher Hindawi Limited
series The Scientific World Journal
issn 2356-6140
1537-744X
publishDate 2014-01-01
description 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.
url http://dx.doi.org/10.1155/2014/347043
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