Bayes mortality: a package in R for bayesian graduation of mortality rates

Detalhes bibliográficos
Autor(a) principal: Lobo, Viviana das Graças Ribeiro
Data de Publicação: 2022
Tipo de documento: Artigo
Idioma: por
Título da fonte: Repositório Institucional do FGV (FGV Repositório Digital)
Texto Completo: https://hdl.handle.net/10438/33191
Resumo: We present an R package to adjust and visualize the Heligman-Pollard law via Bayesian approach through Monte Carlo Markov chain techniques. We also present an option to fit a reduced model as well as different closing methods to the mortality graduation. The pack age provides useful tools in order to estimate the parameters of models, plot the mortality curve, and compute some quantities related to life tables for Heligman-Pollard law consid ering three distinct response variables for mortality. Therefore, mortality measurement at advanced ages and extrapolation are considered. Analysis of mortality rates is considered using the Human Mortality Dataset to show the applications of functions to graduation. Furthermore, the functions provided by the package could be adaptable by the user.
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spelling Lobo, Viviana das Graças RibeiroDemais unidades::RPCA2023-02-03T15:16:32Z2023-02-03T15:16:32Z2022https://hdl.handle.net/10438/33191We present an R package to adjust and visualize the Heligman-Pollard law via Bayesian approach through Monte Carlo Markov chain techniques. We also present an option to fit a reduced model as well as different closing methods to the mortality graduation. The pack age provides useful tools in order to estimate the parameters of models, plot the mortality curve, and compute some quantities related to life tables for Heligman-Pollard law consid ering three distinct response variables for mortality. Therefore, mortality measurement at advanced ages and extrapolation are considered. Analysis of mortality rates is considered using the Human Mortality Dataset to show the applications of functions to graduation. 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dc.title.por.fl_str_mv Bayes mortality: a package in R for bayesian graduation of mortality rates
title Bayes mortality: a package in R for bayesian graduation of mortality rates
spellingShingle Bayes mortality: a package in R for bayesian graduation of mortality rates
Lobo, Viviana das Graças Ribeiro
Mortality graduation
Bayes mortality
Heligman-Pollard Model
Ciências sociais
title_short Bayes mortality: a package in R for bayesian graduation of mortality rates
title_full Bayes mortality: a package in R for bayesian graduation of mortality rates
title_fullStr Bayes mortality: a package in R for bayesian graduation of mortality rates
title_full_unstemmed Bayes mortality: a package in R for bayesian graduation of mortality rates
title_sort Bayes mortality: a package in R for bayesian graduation of mortality rates
author Lobo, Viviana das Graças Ribeiro
author_facet Lobo, Viviana das Graças Ribeiro
author_role author
dc.contributor.unidadefgv.por.fl_str_mv Demais unidades::RPCA
dc.contributor.author.fl_str_mv Lobo, Viviana das Graças Ribeiro
dc.subject.por.fl_str_mv Mortality graduation
Bayes mortality
Heligman-Pollard Model
topic Mortality graduation
Bayes mortality
Heligman-Pollard Model
Ciências sociais
dc.subject.area.por.fl_str_mv Ciências sociais
description We present an R package to adjust and visualize the Heligman-Pollard law via Bayesian approach through Monte Carlo Markov chain techniques. We also present an option to fit a reduced model as well as different closing methods to the mortality graduation. The pack age provides useful tools in order to estimate the parameters of models, plot the mortality curve, and compute some quantities related to life tables for Heligman-Pollard law consid ering three distinct response variables for mortality. Therefore, mortality measurement at advanced ages and extrapolation are considered. Analysis of mortality rates is considered using the Human Mortality Dataset to show the applications of functions to graduation. Furthermore, the functions provided by the package could be adaptable by the user.
publishDate 2022
dc.date.issued.fl_str_mv 2022
dc.date.accessioned.fl_str_mv 2023-02-03T15:16:32Z
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