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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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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