Identification and forecasting in mortality models
type: Item
instance of: scholarly article
title: Identification and forecasting in mortality models
zbMATH Open document ID: 1328.62575
DOI: 10.1155/2014/347043
author: Bent Nielsen
author: Jens Perch Nielsen
published in: The Scientific World Journal. Probability and Statistics
publication date: 2016-01-13T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 62P05
Mathematics Subject Classification ID: 91B30
zbMATH DE Number: 6529675
Wikidata QID: Q21284715
describes a project that uses: R
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/347043
OpenAlex ID: W2148903896
cites work: Modelling adult mortuality in small populations the saint model
cites work: Modeling and Forecasting U.S. Mortality
cites work: Q3611830
cites work: Q4101268
cites work: Q4167890
cites work: Q3218902
cites work: Bayes estimates in one-way and two-way models
cites work: Q4948699
cites work: Identification of the age-period-cohort model and the extended chain-ladder model
cites work: Statistical inference in the Lexis diagram
cites work: Forecasting with the age-period-cohort model and the extended chain-ladder model
cites work: A Gravity Model of Mortality Rates for Two Related Populations
cites work: Likelihood-Based Inference in Cointegrated Vector Autoregressive Models
cites work: The likelihood-ratio test for rank in bivariate canonical correlation analysis
cites work: Conditional test for rank in bivariate canonical correlation analysis
cites work: A cohort-based extension to the Lee-Carter model for mortality reduction factors
cites work: A Bayesian forecasting model: predicting U.S. male mortality
cites work: Co-Integration and Error Correction: Representation, Estimation, and Testing
cites work: Q3433854
cites work: Q3359644
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description: scientific article; zbMATH DE number 6529675