Palavras-chave
outliers
busca de projeções
curtose
empresas argentinas
busca de projeções
curtose
empresas argentinas
Como Citar
Stimolo, M. I., & Ortiz, P. A. (2020). Algoritmos de busca de projeção para detectar valores atípicos. Cuadernos De Administración, 33. https://doi.org/10.11144/Javeriana.cao33.ppado
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Resumo
Este trabalho compara os métodos propostos por Peña e Prieto (2001), e Filzmoser, Maronna e Werner (2008) para detectar dados atípicos em empresas argentinas que cotizam suas ações no Mercado de Valores. A heterogeneidade significativa entre observações pode ser uma consequência da presença de dados atípicos. A detecção de dados atípicos é importante na análise estatística por seu efeito na distorção das medidas descritivas e nos estimadores dos parâmetros. Existem distintos métodos multivariados para detectar dados atípicos, tais como os métodos baseados na distância ou os métodos de busca de projeções.
Anderson, M., Banker, R., & Janakiraman, S. (2003). Are selling, general, and administrative costs “sticky”? Journal of Accounting Research, 41(1), 47-63. https://doi.org/10.1111/1475-679X.00095
Banker, R., & Byzalov, D. (2014). Asymmetric cost behavior. Journal of Management Accounting Research, 26(2), 43-79. https://doi.org/10.2308/jmar-50846
Bolsar (s.f). Buenos Aires Stock Exchange. https://www.bolsar.com/VistasDL/PaginaPrincipal.aspx
Campbell, N. A. (1980). Robust procedures in multivariate analysis I: Robust covariance estimation. Applied statistics, 231-237. https://doi.org/10.2307/2346896
Filzmoser, P. (2015). Gschwandtner M. mvoutlier: Multivariate outlier detection based on robust methods. R package version 2.0.6.In. Routine available at http://halweb.uc3m.es/esp/Personal/personas/fjp/research.htm
Filzmoser, P., Maronna, R., & Werner, M. (2008). Outlier identification in high dimensions. Computational Statistics & Data Analysis, 52(3), 1694-1711. https://doi.org/10.1016/j.csda.2007.05.018
Gnanadesikan, R., & Kettenring, J. (1972). Robust estimates, residuals, and outlier detection with multiresponse data. Biometrics, 81-124. https://doi.org/10.2307/2528963
Maronna, R. A. (1976). Robust M-estimators of multivariate location and scatter. The Annals of Statistics, 51-67. https://doi.org/10.1214/aos/1176343347
Maronna, R. A., & Zamar, R. H. (2012). Robust estimates of location and dispersion for high-dimensional datasets. Technometrics. https://doi.org/10.1198/004017002188618509
Peña, D. (2002). Análisis de datos multivariantes, vol. 24. Madrid: McGraw-Hill.
Peña, D., & Prieto, F. J. (2001). Multivariate outlier detection and robust covariance matrix estimation. Technometrics, 43(3), 286-310. https://doi.org/10.1198/004017001316975899
Peña, D., & Yohai, V. (1999). A fast procedure for outlier diagnostics in large regression problems. Journal of the American Statistical Association, 94(446), 434-445. https://doi.org/10.1080/01621459.1999.10474138
Rocke, D. M. (1996). Robustness properties of S-estimators of multivariate location and shape in high dimension. The Annals of statistics, 1327-1345. https://doi.org/10.1214/aos/1032526972
Rousseeuw, P. J. (1985). Multivariate estimation with high breakdown point. Mathematical statistics and applications, 8, 283-297. https://www.researchgate.net/profile/Peter_Rousseeuw/publication/239666038_Multivariate_Estimation_With_High_Breakdown_Point/links/0deec53137b8cc68aa000000.pdf
Rousseeuw, P. J. (1993). A resampling design for computing high-breakdown regression. Statistics & probability letters, 18(2), 125-128. https://doi.org/10.1016/0167-7152(93)90180-Q
Rousseeuw, P. J., & Driessen, K. V. (1999). A fast algorithm for the minimum covariance determinant estimator. Technometrics, 41(3), 212-223. https://doi.org/10.1080/00401706.1999.10485670
Stahel, W. A. (1981). Robuste schätzungen: infinitesimale optimalität und schätzungen von kovarianzmatrizen: Eidgenössische Technische Hochschule - ETH. Zürich.
Uriel Jiménez, E., & Aldás Manzano, J. (2005). Análisis multivariante aplicado: aplicaciones al marketing, investigación de mercados, economía, dirección de empresas y turismo. Madrid: Thomson.
Banker, R., & Byzalov, D. (2014). Asymmetric cost behavior. Journal of Management Accounting Research, 26(2), 43-79. https://doi.org/10.2308/jmar-50846
Bolsar (s.f). Buenos Aires Stock Exchange. https://www.bolsar.com/VistasDL/PaginaPrincipal.aspx
Campbell, N. A. (1980). Robust procedures in multivariate analysis I: Robust covariance estimation. Applied statistics, 231-237. https://doi.org/10.2307/2346896
Filzmoser, P. (2015). Gschwandtner M. mvoutlier: Multivariate outlier detection based on robust methods. R package version 2.0.6.In. Routine available at http://halweb.uc3m.es/esp/Personal/personas/fjp/research.htm
Filzmoser, P., Maronna, R., & Werner, M. (2008). Outlier identification in high dimensions. Computational Statistics & Data Analysis, 52(3), 1694-1711. https://doi.org/10.1016/j.csda.2007.05.018
Gnanadesikan, R., & Kettenring, J. (1972). Robust estimates, residuals, and outlier detection with multiresponse data. Biometrics, 81-124. https://doi.org/10.2307/2528963
Maronna, R. A. (1976). Robust M-estimators of multivariate location and scatter. The Annals of Statistics, 51-67. https://doi.org/10.1214/aos/1176343347
Maronna, R. A., & Zamar, R. H. (2012). Robust estimates of location and dispersion for high-dimensional datasets. Technometrics. https://doi.org/10.1198/004017002188618509
Peña, D. (2002). Análisis de datos multivariantes, vol. 24. Madrid: McGraw-Hill.
Peña, D., & Prieto, F. J. (2001). Multivariate outlier detection and robust covariance matrix estimation. Technometrics, 43(3), 286-310. https://doi.org/10.1198/004017001316975899
Peña, D., & Yohai, V. (1999). A fast procedure for outlier diagnostics in large regression problems. Journal of the American Statistical Association, 94(446), 434-445. https://doi.org/10.1080/01621459.1999.10474138
Rocke, D. M. (1996). Robustness properties of S-estimators of multivariate location and shape in high dimension. The Annals of statistics, 1327-1345. https://doi.org/10.1214/aos/1032526972
Rousseeuw, P. J. (1985). Multivariate estimation with high breakdown point. Mathematical statistics and applications, 8, 283-297. https://www.researchgate.net/profile/Peter_Rousseeuw/publication/239666038_Multivariate_Estimation_With_High_Breakdown_Point/links/0deec53137b8cc68aa000000.pdf
Rousseeuw, P. J. (1993). A resampling design for computing high-breakdown regression. Statistics & probability letters, 18(2), 125-128. https://doi.org/10.1016/0167-7152(93)90180-Q
Rousseeuw, P. J., & Driessen, K. V. (1999). A fast algorithm for the minimum covariance determinant estimator. Technometrics, 41(3), 212-223. https://doi.org/10.1080/00401706.1999.10485670
Stahel, W. A. (1981). Robuste schätzungen: infinitesimale optimalität und schätzungen von kovarianzmatrizen: Eidgenössische Technische Hochschule - ETH. Zürich.
Uriel Jiménez, E., & Aldás Manzano, J. (2005). Análisis multivariante aplicado: aplicaciones al marketing, investigación de mercados, economía, dirección de empresas y turismo. Madrid: Thomson.
Este trabalho está licenciado sob uma licença Creative Commons Attribution 4.0 International License.
Copyright (c) 2020 Maria Inés Stimolo, Pablo Arnaldo Ortiz