Principal components in the discrimination of outliers: a study in simulation sample data corrected by Pearson's and Yates´s chi-square distance

Detalhes bibliográficos
Autor(a) principal: Veloso, Manoel Vitor de Souza
Data de Publicação: 2016
Outros Autores: Cirillo, Marcelo Angelo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFLA
Texto Completo: http://repositorio.ufla.br/jspui/handle/1/32713
Resumo: Current study employs Monte Carlo simulation in the building of a significance test to indicate the principal components that best discriminate against outliers. Different sample sizes were generated by multivariate normal distribution with different numbers of variables and correlation structures. Corrections by chi-square distance of Pearson´s and Yates's were provided for each sample size. Pearson´s correlation test showed the best performance. By increasing the number of variables, significance probabilities in favor of hypothesis H0 were reduced. So that the proposed method could be illustrated, a multivariate time series was applied with regard to sales volume rates in the state of Minas Gerais, obtained in different market segments
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spelling Principal components in the discrimination of outliers: a study in simulation sample data corrected by Pearson's and Yates´s chi-square distanceComponentes principais na discriminação de outliers: estudo de simulação em dados amostrais corrigidos pelas distâncias qui-quadrado de Pearson’s and YatesContaminated samplesMonte CarloSignificance testP-valueAmostras contaminadasTeste de significânciaCurrent study employs Monte Carlo simulation in the building of a significance test to indicate the principal components that best discriminate against outliers. Different sample sizes were generated by multivariate normal distribution with different numbers of variables and correlation structures. Corrections by chi-square distance of Pearson´s and Yates's were provided for each sample size. Pearson´s correlation test showed the best performance. By increasing the number of variables, significance probabilities in favor of hypothesis H0 were reduced. So that the proposed method could be illustrated, a multivariate time series was applied with regard to sales volume rates in the state of Minas Gerais, obtained in different market segmentsEste trabalho tem por objetivo realizar um estudo, utilizando simulação Monte Carlo na construção de um teste de significância para indicar os componentes principais que melhor discriminam as discrepâncias. Neste contexto, diferentes tamanhos amostrais foram gerados pela distribuição normal multivariada com diferentes números de variáveis e estruturas de correlação. Para cada tamanho amostral, procedeu-se com as correções dadas pela distância qui-quadrado de Pearson e Yates. Concluiu-se ao considerar a correção de Pearson o teste apresentou melhor desempenho, entretanto, aumentando o número de variáveis as probabilidades de significância a favor a hipótese H0 foram reduzidas. Por fim, para ilustrar a metodologia proposta realizou-se uma aplicação em uma série temporal multivariada referente a índices de volumes de vendas do estado de Minas Gerais obtidos em diferentes segmentos de mercadosUniversidade Estadual de Maringá2019-02-04T10:57:55Z2019-02-04T10:57:55Z2016info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfVELOSO, M. V. de S.; CIRILLO, M. A. Principal components in the discrimination of outliers: a study in simulation sample data corrected by Pearson's and Yates´s chi-square distance. Acta Scientiarum-Technology, Maringá, v. 38, n. 2, p. 193-200, Apr./June 2016.http://repositorio.ufla.br/jspui/handle/1/32713Acta Scientiarum-Technologyreponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAAttribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessVeloso, Manoel Vitor de SouzaCirillo, Marcelo Angeloeng2023-05-19T19:03:59Zoai:localhost:1/32713Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2023-05-19T19:03:59Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)false
dc.title.none.fl_str_mv Principal components in the discrimination of outliers: a study in simulation sample data corrected by Pearson's and Yates´s chi-square distance
Componentes principais na discriminação de outliers: estudo de simulação em dados amostrais corrigidos pelas distâncias qui-quadrado de Pearson’s and Yates
title Principal components in the discrimination of outliers: a study in simulation sample data corrected by Pearson's and Yates´s chi-square distance
spellingShingle Principal components in the discrimination of outliers: a study in simulation sample data corrected by Pearson's and Yates´s chi-square distance
Veloso, Manoel Vitor de Souza
Contaminated samples
Monte Carlo
Significance test
P-value
Amostras contaminadas
Teste de significância
title_short Principal components in the discrimination of outliers: a study in simulation sample data corrected by Pearson's and Yates´s chi-square distance
title_full Principal components in the discrimination of outliers: a study in simulation sample data corrected by Pearson's and Yates´s chi-square distance
title_fullStr Principal components in the discrimination of outliers: a study in simulation sample data corrected by Pearson's and Yates´s chi-square distance
title_full_unstemmed Principal components in the discrimination of outliers: a study in simulation sample data corrected by Pearson's and Yates´s chi-square distance
title_sort Principal components in the discrimination of outliers: a study in simulation sample data corrected by Pearson's and Yates´s chi-square distance
author Veloso, Manoel Vitor de Souza
author_facet Veloso, Manoel Vitor de Souza
Cirillo, Marcelo Angelo
author_role author
author2 Cirillo, Marcelo Angelo
author2_role author
dc.contributor.author.fl_str_mv Veloso, Manoel Vitor de Souza
Cirillo, Marcelo Angelo
dc.subject.por.fl_str_mv Contaminated samples
Monte Carlo
Significance test
P-value
Amostras contaminadas
Teste de significância
topic Contaminated samples
Monte Carlo
Significance test
P-value
Amostras contaminadas
Teste de significância
description Current study employs Monte Carlo simulation in the building of a significance test to indicate the principal components that best discriminate against outliers. Different sample sizes were generated by multivariate normal distribution with different numbers of variables and correlation structures. Corrections by chi-square distance of Pearson´s and Yates's were provided for each sample size. Pearson´s correlation test showed the best performance. By increasing the number of variables, significance probabilities in favor of hypothesis H0 were reduced. So that the proposed method could be illustrated, a multivariate time series was applied with regard to sales volume rates in the state of Minas Gerais, obtained in different market segments
publishDate 2016
dc.date.none.fl_str_mv 2016
2019-02-04T10:57:55Z
2019-02-04T10:57:55Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv VELOSO, M. V. de S.; CIRILLO, M. A. Principal components in the discrimination of outliers: a study in simulation sample data corrected by Pearson's and Yates´s chi-square distance. Acta Scientiarum-Technology, Maringá, v. 38, n. 2, p. 193-200, Apr./June 2016.
http://repositorio.ufla.br/jspui/handle/1/32713
identifier_str_mv VELOSO, M. V. de S.; CIRILLO, M. A. Principal components in the discrimination of outliers: a study in simulation sample data corrected by Pearson's and Yates´s chi-square distance. Acta Scientiarum-Technology, Maringá, v. 38, n. 2, p. 193-200, Apr./June 2016.
url http://repositorio.ufla.br/jspui/handle/1/32713
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
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
reponame:Repositório Institucional da UFLA
instname:Universidade Federal de Lavras (UFLA)
instacron:UFLA
instname_str Universidade Federal de Lavras (UFLA)
instacron_str UFLA
institution UFLA
reponame_str Repositório Institucional da UFLA
collection Repositório Institucional da UFLA
repository.name.fl_str_mv Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)
repository.mail.fl_str_mv nivaldo@ufla.br || repositorio.biblioteca@ufla.br
_version_ 1784550159002107904

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