Practical rules for summing the series of the Tweedie probability density function with high-precision arithmetic

Detalhes bibliográficos
Autor(a) principal: DIAS,NELSON L.
Data de Publicação: 2019
Outros Autores: RIBEIRO JR,PAULO J.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Anais da Academia Brasileira de Ciências (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652019000700902
Resumo: Abstract: For some ranges of its parameters and arguments, the series for Tweedie probability density functions are sometimes exceedingly difficult to sum numerically. Existing numerical implementations utilizing inversion techniques and properties of stable distributions can cope with these problems, but no single one is successful in all cases. In this work we investigate heuristically the nature of the problem, and show that it is not related to the order of summation of the terms. Using a variable involved in the analytical proof of convergence of the series, the critical parameter for numerical non-convergence (“alpha”) is identified, and an heuristic criterion is developed to avoid numerical non-convergence for a reasonably large sub-interval of the latter. With these practical rules, simple summation algorithms provide sufficiently robust results for the calculation of the density function and its definite integrals. These implementations need to utilize high-precision arithmetic, and are programmed in the Python programming language. A thorough comparison with existing R functions allows the identification of cases when the latter fail, and provide further guidance to their use.
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spelling Practical rules for summing the series of the Tweedie probability density function with high-precision arithmeticPythonR Tweedie packageTweedie probability densityTweedie seriesAbstract: For some ranges of its parameters and arguments, the series for Tweedie probability density functions are sometimes exceedingly difficult to sum numerically. Existing numerical implementations utilizing inversion techniques and properties of stable distributions can cope with these problems, but no single one is successful in all cases. In this work we investigate heuristically the nature of the problem, and show that it is not related to the order of summation of the terms. Using a variable involved in the analytical proof of convergence of the series, the critical parameter for numerical non-convergence (“alpha”) is identified, and an heuristic criterion is developed to avoid numerical non-convergence for a reasonably large sub-interval of the latter. With these practical rules, simple summation algorithms provide sufficiently robust results for the calculation of the density function and its definite integrals. These implementations need to utilize high-precision arithmetic, and are programmed in the Python programming language. A thorough comparison with existing R functions allows the identification of cases when the latter fail, and provide further guidance to their use.Academia Brasileira de Ciências2019-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652019000700902Anais da Academia Brasileira de Ciências v.91 n.4 2019reponame:Anais da Academia Brasileira de Ciências (Online)instname:Academia Brasileira de Ciências (ABC)instacron:ABC10.1590/0001-3765201920180268info:eu-repo/semantics/openAccessDIAS,NELSON L.RIBEIRO JR,PAULO J.eng2019-12-10T00:00:00Zoai:scielo:S0001-37652019000700902Revistahttp://www.scielo.br/aabchttps://old.scielo.br/oai/scielo-oai.php||aabc@abc.org.br1678-26900001-3765opendoar:2019-12-10T00:00Anais da Academia Brasileira de Ciências (Online) - Academia Brasileira de Ciências (ABC)false
dc.title.none.fl_str_mv Practical rules for summing the series of the Tweedie probability density function with high-precision arithmetic
title Practical rules for summing the series of the Tweedie probability density function with high-precision arithmetic
spellingShingle Practical rules for summing the series of the Tweedie probability density function with high-precision arithmetic
DIAS,NELSON L.
Python
R Tweedie package
Tweedie probability density
Tweedie series
title_short Practical rules for summing the series of the Tweedie probability density function with high-precision arithmetic
title_full Practical rules for summing the series of the Tweedie probability density function with high-precision arithmetic
title_fullStr Practical rules for summing the series of the Tweedie probability density function with high-precision arithmetic
title_full_unstemmed Practical rules for summing the series of the Tweedie probability density function with high-precision arithmetic
title_sort Practical rules for summing the series of the Tweedie probability density function with high-precision arithmetic
author DIAS,NELSON L.
author_facet DIAS,NELSON L.
RIBEIRO JR,PAULO J.
author_role author
author2 RIBEIRO JR,PAULO J.
author2_role author
dc.contributor.author.fl_str_mv DIAS,NELSON L.
RIBEIRO JR,PAULO J.
dc.subject.por.fl_str_mv Python
R Tweedie package
Tweedie probability density
Tweedie series
topic Python
R Tweedie package
Tweedie probability density
Tweedie series
description Abstract: For some ranges of its parameters and arguments, the series for Tweedie probability density functions are sometimes exceedingly difficult to sum numerically. Existing numerical implementations utilizing inversion techniques and properties of stable distributions can cope with these problems, but no single one is successful in all cases. In this work we investigate heuristically the nature of the problem, and show that it is not related to the order of summation of the terms. Using a variable involved in the analytical proof of convergence of the series, the critical parameter for numerical non-convergence (“alpha”) is identified, and an heuristic criterion is developed to avoid numerical non-convergence for a reasonably large sub-interval of the latter. With these practical rules, simple summation algorithms provide sufficiently robust results for the calculation of the density function and its definite integrals. These implementations need to utilize high-precision arithmetic, and are programmed in the Python programming language. A thorough comparison with existing R functions allows the identification of cases when the latter fail, and provide further guidance to their use.
publishDate 2019
dc.date.none.fl_str_mv 2019-01-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652019000700902
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652019000700902
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/0001-3765201920180268
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
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dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Academia Brasileira de Ciências
publisher.none.fl_str_mv Academia Brasileira de Ciências
dc.source.none.fl_str_mv Anais da Academia Brasileira de Ciências v.91 n.4 2019
reponame:Anais da Academia Brasileira de Ciências (Online)
instname:Academia Brasileira de Ciências (ABC)
instacron:ABC
instname_str Academia Brasileira de Ciências (ABC)
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institution ABC
reponame_str Anais da Academia Brasileira de Ciências (Online)
collection Anais da Academia Brasileira de Ciências (Online)
repository.name.fl_str_mv Anais da Academia Brasileira de Ciências (Online) - Academia Brasileira de Ciências (ABC)
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