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Small population bias and sampling effects in stochastic mortality modelling

We propose the use of parametric bootstrap methods to investigate the finite sample distribution of the maximum likelihood estimator for the parameter vector of a stochastic mortality model. Particular emphasis is placed on the effect that the size of the underlying population has on the distributio...

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Detalles Bibliográficos
Autores principales: Chen, Liang, Cairns, Andrew J. G., Kleinow, Torsten
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5744643/
https://www.ncbi.nlm.nih.gov/pubmed/29323361
http://dx.doi.org/10.1007/s13385-016-0143-x
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author Chen, Liang
Cairns, Andrew J. G.
Kleinow, Torsten
author_facet Chen, Liang
Cairns, Andrew J. G.
Kleinow, Torsten
author_sort Chen, Liang
collection PubMed
description We propose the use of parametric bootstrap methods to investigate the finite sample distribution of the maximum likelihood estimator for the parameter vector of a stochastic mortality model. Particular emphasis is placed on the effect that the size of the underlying population has on the distribution of the MLE in finite samples, and on the dependency structure of the resulting estimator: that is, the dependencies between estimators for the age, period and cohort effects in our model. In addition, we study the distribution of a likelihood ratio test statistic where we test a null hypothesis about the true parameters in our model. Finally, we apply the LRT to the cohort effects estimated from observed mortality rates for females in England and Wales and males in Scotland.
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spelling pubmed-57446432018-01-08 Small population bias and sampling effects in stochastic mortality modelling Chen, Liang Cairns, Andrew J. G. Kleinow, Torsten Eur Actuar J Original Research Paper We propose the use of parametric bootstrap methods to investigate the finite sample distribution of the maximum likelihood estimator for the parameter vector of a stochastic mortality model. Particular emphasis is placed on the effect that the size of the underlying population has on the distribution of the MLE in finite samples, and on the dependency structure of the resulting estimator: that is, the dependencies between estimators for the age, period and cohort effects in our model. In addition, we study the distribution of a likelihood ratio test statistic where we test a null hypothesis about the true parameters in our model. Finally, we apply the LRT to the cohort effects estimated from observed mortality rates for females in England and Wales and males in Scotland. Springer Berlin Heidelberg 2017-01-23 2017 /pmc/articles/PMC5744643/ /pubmed/29323361 http://dx.doi.org/10.1007/s13385-016-0143-x Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Original Research Paper
Chen, Liang
Cairns, Andrew J. G.
Kleinow, Torsten
Small population bias and sampling effects in stochastic mortality modelling
title Small population bias and sampling effects in stochastic mortality modelling
title_full Small population bias and sampling effects in stochastic mortality modelling
title_fullStr Small population bias and sampling effects in stochastic mortality modelling
title_full_unstemmed Small population bias and sampling effects in stochastic mortality modelling
title_short Small population bias and sampling effects in stochastic mortality modelling
title_sort small population bias and sampling effects in stochastic mortality modelling
topic Original Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5744643/
https://www.ncbi.nlm.nih.gov/pubmed/29323361
http://dx.doi.org/10.1007/s13385-016-0143-x
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