A Monte Carlo simulation study of a robust estimator used in the inference of a contaminated binomial model - doi: 10.4025/actascitechnol.v32i3.4145

Detalhes bibliográficos
Autor(a) principal: Silva, Augusto Maciel da
Data de Publicação: 2010
Outros Autores: Cirillo, Marcelo Ângelo
Tipo de documento: Artigo
Idioma: por
Título da fonte: Acta scientiarum. Technology (Online)
Texto Completo: http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/4145
Resumo: The statistical inference in binomial population is subject to gross errors of estimate, as the samples are not identically distributed. Due to this problem, this work aims to determine which is the best affinity constant (c1) that provides the best performance in the estimator, belonging to the class of E-estimators. With that purpose, the methodology used in this work was applied considering the Monte Carlo simulation method, in which different configurations described by combination of parametric values, levels of contamination and sample sizes were appraised. It was concluded that for the high probability of contamination (γ = 0.40), c1 = 0.1 is recommended in cases with large samples (n = 50 and n = 80).
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spelling A Monte Carlo simulation study of a robust estimator used in the inference of a contaminated binomial model - doi: 10.4025/actascitechnol.v32i3.4145Estudo por simulação Monte Carlo de um estimador robusto utilizado na inferência de um modelo binomial contaminado - doi: 10.4025/actascitechnol.v32i3.4145binomial distributioncontaminated binomialMonte Carlorobustnessdistribuição binomialbinomiais contaminadasMonte CarlorobustezEstatísticaThe statistical inference in binomial population is subject to gross errors of estimate, as the samples are not identically distributed. Due to this problem, this work aims to determine which is the best affinity constant (c1) that provides the best performance in the estimator, belonging to the class of E-estimators. With that purpose, the methodology used in this work was applied considering the Monte Carlo simulation method, in which different configurations described by combination of parametric values, levels of contamination and sample sizes were appraised. It was concluded that for the high probability of contamination (γ = 0.40), c1 = 0.1 is recommended in cases with large samples (n = 50 and n = 80).A inferência estatística em populações binomiais contaminadas está sujeita a erros grosseiros de estimação, uma vez que as amostras não são identicamente distribuídas. Por esse problema, este trabalho tem por objetivo determinar qual a melhor constante de afinidade (c1) que proporcione melhor desempenho em um estimador pertencente à classe dos estimadores-E. Com esse propósito, neste trabalho, foi utilizada a metodologia, considerando-se o método de simulação Monte Carlo, no qual diferentes configurações descritas pela combinação de valores paramétricos, níveis de contaminação e tamanhos de amostra foram avaliados. Concluiu-se que, para alta probabilidade de mistura (γ = 0,40), recomenda-se assumir c1 = 0,1 nas situações de grandes amostras (n = 50 e n = 80).Universidade Estadual De Maringá2010-11-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/414510.4025/actascitechnol.v32i3.4145Acta Scientiarum. Technology; Vol 32 No 3 (2010); 303-307Acta Scientiarum. Technology; v. 32 n. 3 (2010); 303-3071806-25631807-8664reponame:Acta scientiarum. Technology (Online)instname:Universidade Estadual de Maringá (UEM)instacron:UEMporhttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/4145/4145Silva, Augusto Maciel daCirillo, Marcelo Ângeloinfo:eu-repo/semantics/openAccess2024-05-17T13:03:01Zoai:periodicos.uem.br/ojs:article/4145Revistahttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/indexPUBhttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/oai||actatech@uem.br1807-86641806-2563opendoar:2024-05-17T13:03:01Acta scientiarum. Technology (Online) - Universidade Estadual de Maringá (UEM)false
dc.title.none.fl_str_mv A Monte Carlo simulation study of a robust estimator used in the inference of a contaminated binomial model - doi: 10.4025/actascitechnol.v32i3.4145
Estudo por simulação Monte Carlo de um estimador robusto utilizado na inferência de um modelo binomial contaminado - doi: 10.4025/actascitechnol.v32i3.4145
title A Monte Carlo simulation study of a robust estimator used in the inference of a contaminated binomial model - doi: 10.4025/actascitechnol.v32i3.4145
spellingShingle A Monte Carlo simulation study of a robust estimator used in the inference of a contaminated binomial model - doi: 10.4025/actascitechnol.v32i3.4145
Silva, Augusto Maciel da
binomial distribution
contaminated binomial
Monte Carlo
robustness
distribuição binomial
binomiais contaminadas
Monte Carlo
robustez
Estatística
title_short A Monte Carlo simulation study of a robust estimator used in the inference of a contaminated binomial model - doi: 10.4025/actascitechnol.v32i3.4145
title_full A Monte Carlo simulation study of a robust estimator used in the inference of a contaminated binomial model - doi: 10.4025/actascitechnol.v32i3.4145
title_fullStr A Monte Carlo simulation study of a robust estimator used in the inference of a contaminated binomial model - doi: 10.4025/actascitechnol.v32i3.4145
title_full_unstemmed A Monte Carlo simulation study of a robust estimator used in the inference of a contaminated binomial model - doi: 10.4025/actascitechnol.v32i3.4145
title_sort A Monte Carlo simulation study of a robust estimator used in the inference of a contaminated binomial model - doi: 10.4025/actascitechnol.v32i3.4145
author Silva, Augusto Maciel da
author_facet Silva, Augusto Maciel da
Cirillo, Marcelo Ângelo
author_role author
author2 Cirillo, Marcelo Ângelo
author2_role author
dc.contributor.author.fl_str_mv Silva, Augusto Maciel da
Cirillo, Marcelo Ângelo
dc.subject.por.fl_str_mv binomial distribution
contaminated binomial
Monte Carlo
robustness
distribuição binomial
binomiais contaminadas
Monte Carlo
robustez
Estatística
topic binomial distribution
contaminated binomial
Monte Carlo
robustness
distribuição binomial
binomiais contaminadas
Monte Carlo
robustez
Estatística
description The statistical inference in binomial population is subject to gross errors of estimate, as the samples are not identically distributed. Due to this problem, this work aims to determine which is the best affinity constant (c1) that provides the best performance in the estimator, belonging to the class of E-estimators. With that purpose, the methodology used in this work was applied considering the Monte Carlo simulation method, in which different configurations described by combination of parametric values, levels of contamination and sample sizes were appraised. It was concluded that for the high probability of contamination (γ = 0.40), c1 = 0.1 is recommended in cases with large samples (n = 50 and n = 80).
publishDate 2010
dc.date.none.fl_str_mv 2010-11-09
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/4145
10.4025/actascitechnol.v32i3.4145
url http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/4145
identifier_str_mv 10.4025/actascitechnol.v32i3.4145
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/4145/4145
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade Estadual De Maringá
publisher.none.fl_str_mv Universidade Estadual De Maringá
dc.source.none.fl_str_mv Acta Scientiarum. Technology; Vol 32 No 3 (2010); 303-307
Acta Scientiarum. Technology; v. 32 n. 3 (2010); 303-307
1806-2563
1807-8664
reponame:Acta scientiarum. Technology (Online)
instname:Universidade Estadual de Maringá (UEM)
instacron:UEM
instname_str Universidade Estadual de Maringá (UEM)
instacron_str UEM
institution UEM
reponame_str Acta scientiarum. Technology (Online)
collection Acta scientiarum. Technology (Online)
repository.name.fl_str_mv Acta scientiarum. Technology (Online) - Universidade Estadual de Maringá (UEM)
repository.mail.fl_str_mv ||actatech@uem.br
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