The overlooked potential of generalized linear models in astronomy – III. Bayesian negative binomial regression and globular cluster populations
Autor(a) principal: | |
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Data de Publicação: | 2015 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRGS |
Texto Completo: | http://hdl.handle.net/10183/140345 |
Resumo: | In this paper, the third in a series illustrating the power of generalized linear models (GLMs) for the astronomical community, we elucidate the potential of the class of GLMs which handles count data. The size of a galaxy’s globular cluster (GC) population (NGC) is a prolonged puzzle in the astronomical literature. It falls in the category of count data analysis, yet it is usually modelled as if it were a continuous response variable. We have developed a Bayesian negative binomial regression model to study the connection between NGC and the following galaxy properties: central black hole mass, dynamical bulge mass, bulge velocity dispersion and absolute visual magnitude. The methodology introduced herein naturally accounts for heteroscedasticity, intrinsic scatter, errors in measurements in both axes (either discrete or continuous) and allows modelling the population of GCs on their natural scale as a nonnegative integer variable. Prediction intervals of 99 per cent around the trend for expected NGC comfortably envelope the data, notably including the Milky Way, which has hitherto been considered a problematic outlier. Finally, we demonstrate how random intercept models can incorporate information of each particular galaxy morphological type. Bayesian variable selection methodology allows for automatically identifying galaxy types with different productions of GCs, suggesting that on average S0 galaxies have a GC population 35 per cent smaller than other types with similar brightness. |
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Souza, Rafael da Silva deHilbe, JosephBuelens, BartRiggs, J. D.Cameron, EwanIshida, Emille Eugenia de OliveiraChies-Santos, Ana LeonorKilledar, Madhura2016-05-06T02:21:45Z20150035-8711http://hdl.handle.net/10183/140345000985381In this paper, the third in a series illustrating the power of generalized linear models (GLMs) for the astronomical community, we elucidate the potential of the class of GLMs which handles count data. The size of a galaxy’s globular cluster (GC) population (NGC) is a prolonged puzzle in the astronomical literature. It falls in the category of count data analysis, yet it is usually modelled as if it were a continuous response variable. We have developed a Bayesian negative binomial regression model to study the connection between NGC and the following galaxy properties: central black hole mass, dynamical bulge mass, bulge velocity dispersion and absolute visual magnitude. The methodology introduced herein naturally accounts for heteroscedasticity, intrinsic scatter, errors in measurements in both axes (either discrete or continuous) and allows modelling the population of GCs on their natural scale as a nonnegative integer variable. Prediction intervals of 99 per cent around the trend for expected NGC comfortably envelope the data, notably including the Milky Way, which has hitherto been considered a problematic outlier. Finally, we demonstrate how random intercept models can incorporate information of each particular galaxy morphological type. Bayesian variable selection methodology allows for automatically identifying galaxy types with different productions of GCs, suggesting that on average S0 galaxies have a GC population 35 per cent smaller than other types with similar brightness.application/pdfengMonthly notices of the Royal Astronomical Society. Oxford. Vol. 453, no. 2 (Oct. 2015), p. 1928-1940Aglomerados globularesEstatística aplicadaMethods: data analysisMethods: statisticalGlobular clusters: generalThe overlooked potential of generalized linear models in astronomy – III. Bayesian negative binomial regression and globular cluster populationsEstrangeiroinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSORIGINAL000985381.pdf000985381.pdfTexto completo (inglês)application/pdf2128187http://www.lume.ufrgs.br/bitstream/10183/140345/1/000985381.pdf8b424c4702c652a90910db5aaf8a4053MD51TEXT000985381.pdf.txt000985381.pdf.txtExtracted Texttext/plain58535http://www.lume.ufrgs.br/bitstream/10183/140345/2/000985381.pdf.txteed38fc16356248b0192ac92f8bf0985MD52THUMBNAIL000985381.pdf.jpg000985381.pdf.jpgGenerated Thumbnailimage/jpeg2241http://www.lume.ufrgs.br/bitstream/10183/140345/3/000985381.pdf.jpg0b0c7be595e243ce5201b948ef12c503MD5310183/1403452021-09-18 04:36:14.391015oai:www.lume.ufrgs.br:10183/140345Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2021-09-18T07:36:14Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.fl_str_mv |
The overlooked potential of generalized linear models in astronomy – III. Bayesian negative binomial regression and globular cluster populations |
title |
The overlooked potential of generalized linear models in astronomy – III. Bayesian negative binomial regression and globular cluster populations |
spellingShingle |
The overlooked potential of generalized linear models in astronomy – III. Bayesian negative binomial regression and globular cluster populations Souza, Rafael da Silva de Aglomerados globulares Estatística aplicada Methods: data analysis Methods: statistical Globular clusters: general |
title_short |
The overlooked potential of generalized linear models in astronomy – III. Bayesian negative binomial regression and globular cluster populations |
title_full |
The overlooked potential of generalized linear models in astronomy – III. Bayesian negative binomial regression and globular cluster populations |
title_fullStr |
The overlooked potential of generalized linear models in astronomy – III. Bayesian negative binomial regression and globular cluster populations |
title_full_unstemmed |
The overlooked potential of generalized linear models in astronomy – III. Bayesian negative binomial regression and globular cluster populations |
title_sort |
The overlooked potential of generalized linear models in astronomy – III. Bayesian negative binomial regression and globular cluster populations |
author |
Souza, Rafael da Silva de |
author_facet |
Souza, Rafael da Silva de Hilbe, Joseph Buelens, Bart Riggs, J. D. Cameron, Ewan Ishida, Emille Eugenia de Oliveira Chies-Santos, Ana Leonor Killedar, Madhura |
author_role |
author |
author2 |
Hilbe, Joseph Buelens, Bart Riggs, J. D. Cameron, Ewan Ishida, Emille Eugenia de Oliveira Chies-Santos, Ana Leonor Killedar, Madhura |
author2_role |
author author author author author author author |
dc.contributor.author.fl_str_mv |
Souza, Rafael da Silva de Hilbe, Joseph Buelens, Bart Riggs, J. D. Cameron, Ewan Ishida, Emille Eugenia de Oliveira Chies-Santos, Ana Leonor Killedar, Madhura |
dc.subject.por.fl_str_mv |
Aglomerados globulares Estatística aplicada |
topic |
Aglomerados globulares Estatística aplicada Methods: data analysis Methods: statistical Globular clusters: general |
dc.subject.eng.fl_str_mv |
Methods: data analysis Methods: statistical Globular clusters: general |
description |
In this paper, the third in a series illustrating the power of generalized linear models (GLMs) for the astronomical community, we elucidate the potential of the class of GLMs which handles count data. The size of a galaxy’s globular cluster (GC) population (NGC) is a prolonged puzzle in the astronomical literature. It falls in the category of count data analysis, yet it is usually modelled as if it were a continuous response variable. We have developed a Bayesian negative binomial regression model to study the connection between NGC and the following galaxy properties: central black hole mass, dynamical bulge mass, bulge velocity dispersion and absolute visual magnitude. The methodology introduced herein naturally accounts for heteroscedasticity, intrinsic scatter, errors in measurements in both axes (either discrete or continuous) and allows modelling the population of GCs on their natural scale as a nonnegative integer variable. Prediction intervals of 99 per cent around the trend for expected NGC comfortably envelope the data, notably including the Milky Way, which has hitherto been considered a problematic outlier. Finally, we demonstrate how random intercept models can incorporate information of each particular galaxy morphological type. Bayesian variable selection methodology allows for automatically identifying galaxy types with different productions of GCs, suggesting that on average S0 galaxies have a GC population 35 per cent smaller than other types with similar brightness. |
publishDate |
2015 |
dc.date.issued.fl_str_mv |
2015 |
dc.date.accessioned.fl_str_mv |
2016-05-06T02:21:45Z |
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dc.relation.ispartof.pt_BR.fl_str_mv |
Monthly notices of the Royal Astronomical Society. Oxford. Vol. 453, no. 2 (Oct. 2015), p. 1928-1940 |
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openAccess |
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