Exact critical values for one-way fixed effects models with random sample sizes

Detalhes bibliográficos
Autor(a) principal: Nunes, Célia
Data de Publicação: 2019
Outros Autores: Capistrano, Gilberto, Ferreira, Dário, Ferreira, Sandra S., Mexia, João T.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.6/9065
Resumo: Analysis of variance (ANOVA) is one of the most frequently used statistical analyses in several research areas, namely in medical research. Despite its wide use, it has been applied assuming that sample dimensions are known. In this work we aim to carry out ANOVA like analysis of one-way fixed effects models, to situations where the samples sizes may not be previously known. In these situations it is more appropriate to consider the sample sizes as realizations of independent random variables. This approach must be based on an adequate choice of the distributions of the samples sizes. We assume the Poisson distribution when the occurrence of observations corresponds to a counting process. The Binomial distribution is the proper choice if we have observations failures and there exist an upper bound for the sample sizes. We also show how to carry out our main goal by computing correct critical values. The applicability of the proposed approach is illustrated considering a real data example on cancer registries. The results obtained suggested that false rejections may be avoided by applying our approach.
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spelling Exact critical values for one-way fixed effects models with random sample sizesANOVARandom sample sizesFixed effects modelsCorrect critical valuesCancer registriesAnalysis of variance (ANOVA) is one of the most frequently used statistical analyses in several research areas, namely in medical research. Despite its wide use, it has been applied assuming that sample dimensions are known. In this work we aim to carry out ANOVA like analysis of one-way fixed effects models, to situations where the samples sizes may not be previously known. In these situations it is more appropriate to consider the sample sizes as realizations of independent random variables. This approach must be based on an adequate choice of the distributions of the samples sizes. We assume the Poisson distribution when the occurrence of observations corresponds to a counting process. The Binomial distribution is the proper choice if we have observations failures and there exist an upper bound for the sample sizes. We also show how to carry out our main goal by computing correct critical values. The applicability of the proposed approach is illustrated considering a real data example on cancer registries. The results obtained suggested that false rejections may be avoided by applying our approach.uBibliorumNunes, CéliaCapistrano, GilbertoFerreira, DárioFerreira, Sandra S.Mexia, João T.2020-02-06T12:23:22Z20192019-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.6/9065eng10.1016/j.cam.2018.05.057info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-12-15T09:49:41Zoai:ubibliorum.ubi.pt:10400.6/9065Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:49:18.403407Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Exact critical values for one-way fixed effects models with random sample sizes
title Exact critical values for one-way fixed effects models with random sample sizes
spellingShingle Exact critical values for one-way fixed effects models with random sample sizes
Nunes, Célia
ANOVA
Random sample sizes
Fixed effects models
Correct critical values
Cancer registries
title_short Exact critical values for one-way fixed effects models with random sample sizes
title_full Exact critical values for one-way fixed effects models with random sample sizes
title_fullStr Exact critical values for one-way fixed effects models with random sample sizes
title_full_unstemmed Exact critical values for one-way fixed effects models with random sample sizes
title_sort Exact critical values for one-way fixed effects models with random sample sizes
author Nunes, Célia
author_facet Nunes, Célia
Capistrano, Gilberto
Ferreira, Dário
Ferreira, Sandra S.
Mexia, João T.
author_role author
author2 Capistrano, Gilberto
Ferreira, Dário
Ferreira, Sandra S.
Mexia, João T.
author2_role author
author
author
author
dc.contributor.none.fl_str_mv uBibliorum
dc.contributor.author.fl_str_mv Nunes, Célia
Capistrano, Gilberto
Ferreira, Dário
Ferreira, Sandra S.
Mexia, João T.
dc.subject.por.fl_str_mv ANOVA
Random sample sizes
Fixed effects models
Correct critical values
Cancer registries
topic ANOVA
Random sample sizes
Fixed effects models
Correct critical values
Cancer registries
description Analysis of variance (ANOVA) is one of the most frequently used statistical analyses in several research areas, namely in medical research. Despite its wide use, it has been applied assuming that sample dimensions are known. In this work we aim to carry out ANOVA like analysis of one-way fixed effects models, to situations where the samples sizes may not be previously known. In these situations it is more appropriate to consider the sample sizes as realizations of independent random variables. This approach must be based on an adequate choice of the distributions of the samples sizes. We assume the Poisson distribution when the occurrence of observations corresponds to a counting process. The Binomial distribution is the proper choice if we have observations failures and there exist an upper bound for the sample sizes. We also show how to carry out our main goal by computing correct critical values. The applicability of the proposed approach is illustrated considering a real data example on cancer registries. The results obtained suggested that false rejections may be avoided by applying our approach.
publishDate 2019
dc.date.none.fl_str_mv 2019
2019-01-01T00:00:00Z
2020-02-06T12:23:22Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.6/9065
url http://hdl.handle.net/10400.6/9065
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1016/j.cam.2018.05.057
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
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instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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