Detecting influential observations in principal components and common principal components
Detecting outlying observations is an important step in any analysis, even when robust estimates are used. In particular, the robustified Mahalanobis distance is a natural measure of outlyingness if one focuses on ellipsoidal distributions. However, it is well known that the asymptotic chi-square ap...
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Elsevier B.V.
2010
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| Acceso en línea: | Registro en Scopus DOI Handle Registro en la Biblioteca Digital |
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| LEADER | 08000caa a22007817a 4500 | ||
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| 100 | 1 | |a Boente, G. | |
| 245 | 1 | 0 | |a Detecting influential observations in principal components and common principal components |
| 260 | |b Elsevier B.V. |c 2010 | ||
| 270 | 1 | 0 | |m Boente, G.; Instituto de Cálculo, Ciudad Universitaria, Pabellón 2, Buenos Aires, C1428EHA, Argentina; email: gboente@dm.uba.ar |
| 506 | |2 openaire |e Política editorial | ||
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| 504 | |a Boente, G., Pires, A.M., Rodrigues, I.M., Influence functions and outlier detection under the common principal components model: A robust approach (2002) Biometrika, 89 (4), pp. 861-875. , DOI 10.1093/biomet/89.4.861 | ||
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| 520 | 3 | |a Detecting outlying observations is an important step in any analysis, even when robust estimates are used. In particular, the robustified Mahalanobis distance is a natural measure of outlyingness if one focuses on ellipsoidal distributions. However, it is well known that the asymptotic chi-square approximation for the cutoff value of the Mahalanobis distance based on several robust estimates (like the minimum volume ellipsoid, the minimum covariance determinant and the S-estimators) is not adequate for detecting atypical observations in small samples from the normal distribution. In the multi-population setting and under a common principal components model, aggregated measures based on standardized empirical influence functions are used to detect observations with a significant impact on the estimators. As in the one-population setting, the cutoff values obtained from the asymptotic distribution of those aggregated measures are not adequate for small samples. More appropriate cutoff values, adapted to the sample sizes, can be computed by using a cross-validation approach. Cutoff values obtained from a Monte Carlo study using S-estimators are provided for illustration. A real data set is also analyzed. © 2010 Elsevier B.V. All rights reserved. |l eng | |
| 593 | |a Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Argentina | ||
| 593 | |a CONICET, Argentina | ||
| 593 | |a Departamento de Matemática and CEMAT, Instituto Superior Técnico, Technical University of Lisbon (TULisbon), Lisboa, Portugal | ||
| 690 | 1 | 0 | |a COMMON PRINCIPAL COMPONENTS |
| 690 | 1 | 0 | |a DETECTION OF OUTLIERS |
| 690 | 1 | 0 | |a INFLUENCE FUNCTIONS |
| 690 | 1 | 0 | |a ROBUST ESTIMATION |
| 690 | 1 | 0 | |a MULTIVARIABLE SYSTEMS |
| 690 | 1 | 0 | |a NORMAL DISTRIBUTION |
| 690 | 1 | 0 | |a ASYMPTOTIC DISTRIBUTIONS |
| 690 | 1 | 0 | |a INFLUENCE FUNCTIONS |
| 690 | 1 | 0 | |a INFLUENTIAL OBSERVATIONS |
| 690 | 1 | 0 | |a MAHALANOBIS DISTANCES |
| 690 | 1 | 0 | |a MINIMUM COVARIANCE DETERMINANT |
| 690 | 1 | 0 | |a MINIMUM VOLUME ELLIPSOIDS |
| 690 | 1 | 0 | |a PRINCIPAL COMPONENTS |
| 690 | 1 | 0 | |a ROBUST ESTIMATION |
| 690 | 1 | 0 | |a METHOD OF MOMENTS |
| 700 | 1 | |a Pires, A.M. | |
| 700 | 1 | |a Rodrigues, I.M. | |
| 773 | 0 | |d Elsevier B.V., 2010 |g v. 54 |h pp. 2967-2975 |k n. 12 |p Comput. Stat. Data Anal. |x 01679473 |w (AR-BaUEN)CENRE-4276 |t Computational Statistics and Data Analysis | |
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