Principal components in the discrimination of outliers: a study in simulation sample data corrected by Pearson's and Yates´s chi-square distance
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Data de Publicação: | 2016 |
Outros Autores: | |
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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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_ |
1807835154330157056 |