Robust functional principal component analysis
When dealing with multivariate data robust principal component analysis (PCA), like classical PCA, searches for directions with maximal dispersion of the data projected on it. Instead of using the variance as a measure of dispersion, a robust scale estimator sn may be used in the maximization proble...
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| Formato: | Capítulo de libro |
| Lenguaje: | Inglés |
| Publicado: |
Springer International Publishing
2014
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| Acceso en línea: | Registro en Scopus DOI Handle Registro en la Biblioteca Digital |
| Aporte de: | Registro referencial: Solicitar el recurso aquí |
| LEADER | 07264caa a22006977a 4500 | ||
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| 001 | PAPER-23993 | ||
| 003 | AR-BaUEN | ||
| 005 | 20241002110739.0 | ||
| 008 | 190411s2014 xx ||||fo|||| 00| 0 eng|d | ||
| 024 | 7 | |2 scopus |a 2-s2.0-85030637585 | |
| 040 | |a Scopus |b spa |c AR-BaUEN |d AR-BaUEN | ||
| 100 | 1 | |a Bali, Juan Lucas | |
| 245 | 1 | 0 | |a Robust functional principal component analysis |
| 260 | |b Springer International Publishing |c 2014 | ||
| 270 | 1 | 0 | |m Bali, J.L.; Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires and CONICETArgentina; email: lbali@dm.uba.ar |
| 504 | |a Bali, J.L., Boente, G., Tyler, D.E., Wang, J.L., Robust functional principal components: A projection-pursuit approach (2011) Ann. Stat., 39, pp. 2852-2882 | ||
| 504 | |a Boente, G., Fraiman, R., Discussion on robust principal component analysis for functional data by N. Locantore, J. Marron, D. Simpson, N. Tripoli, J. Zhang and K. Cohen (1999) Test, 8, pp. 28-35 | ||
| 504 | |a Boente, G., Fraiman, R., Kernel-based functional principal components (2000) Stat. Prob. Lett., 48, pp. 335-345 | ||
| 504 | |a Croux, C., Filzmoser, P., Oliveira, M.R., Algorithms for projection-pursuit robust principal component analysis (2007) Chem. Intell. Lab. Syst., 87, pp. 218-225 | ||
| 504 | |a Croux, C., Ruiz-Gazen, A., A fast algorithm for robust principal components based on projection pursuit (1996) Compstat: Proceedings in Computational Statistics, pp. 211-217. , Prat, A. (ed.), Physica-Verlag, Heidelberg | ||
| 504 | |a Croux, C., Ruiz-Gazen, A., High breakdown estimators for principal components: The projection-pursuit approach revisited (2005) J. Multivar. Anal., 95, pp. 206-226 | ||
| 504 | |a Cui, H., He, X., Ng, K.W., Asymptotic distribution of principal components based on robust dispersions (2003) Biometrika, 90, pp. 953-966 | ||
| 504 | |a Dauxois, J., Pousse, A., Romain, Y., Asymptotic theory for the principal component analysis of a vector random function: Some applications to statistical inference (1982) J. Multivar. Anal., 12, pp. 136-154 | ||
| 504 | |a Gervini, D., Robust functional estimation using the spatial median and spherical principal components (2008) Biometrika, 95, pp. 587-600 | ||
| 504 | |a Gervini, D., Detecting and handling outlying trajectories in irregularly sampled functional datasets (2009) Ann. Appl. Stat., 3, pp. 1758-1775 | ||
| 504 | |a Gervini, D., Outlier detection and trimmed estimation for general functional data (2012) Stat. Sin. | ||
| 504 | |a Hall, P., Hosseini-Nasab, M., On properties of functional principal components analysis (2006) J. R. Stat. Soc. Ser. B, 68, pp. 109-126 | ||
| 504 | |a Hall, P., Müller, H.-G., Wang, J.-L., Properties of principal component methods for functional and longitudinal data analysis (2006) Ann. Stat., 34, pp. 1493-1517 | ||
| 504 | |a Hubert, M., Vandervieren, E., An adjusted boxplot for skewed distributions (2008) Comput. Stat. Data Anal., 52, pp. 5186-5201 | ||
| 504 | |a Hyndman, R.J., Ullah, S., Robust forecasting of mortality and fertility rates: A functional data approach (2007) Comput. Stat. Data Anal., 51, pp. 4942-4956 | ||
| 504 | |a Li, G., Chen, Z., Projection-pursuit approach to robust dispersion matrices and principal components: Primary theory and Monte Carlo (1985) J. Am. Stat. Assoc., 80, pp. 759-766 | ||
| 504 | |a Locantore, N., Marron, J.S., Simpson, D.G., Tripoli, N., Zhang, J.T., Cohen, K.L., Robust principal components for functional data (With discussion) (1999) Test, 8, pp. 1-73 | ||
| 504 | |a Malfait, N., Ramsay, J.O., The historical functional linear model (2003) Can. J. Stat., 31, pp. 115-128 | ||
| 504 | |a Pezzulli, S.D., Silverman, B.W., Some properties of smoothed principal components analysis for functional data (1993) Comput. Stat., 8, pp. 1-16 | ||
| 504 | |a Rice, J., Silverman, B.W., Estimating the mean and covariance structure nonparametrically when the data are curves (1991) J. R. Stat. Soc. Ser. B, 53, pp. 233-243 | ||
| 504 | |a Sawant, P., Billor, N., Shin, H., Functional outlier detection with robust functional principal component analysis (2011) Comput. Stat., 27, pp. 83-102 | ||
| 504 | |a Silverman, B.W., Smoothed functional principal components analysis by choice of norm (1996) Ann. Stat., 24, pp. 1-24 | ||
| 504 | |a Sun, Y., Genton, M.G., Functional boxplots (2011) J. Comput. Graph. Stat., 20, pp. 316-334 | ||
| 504 | |a Tyler, D., A note on multivariate location and scatter statistics for sparse data sets (2010) Stat. Prob. Lett., 80, pp. 1409-1413 | ||
| 504 | |a Yao, F., Lee, T.C.M., Penalized spline models for functional principal component analysis (2006) J. R. Stat. Soc. Ser. B, 68, pp. 3-25 | ||
| 504 | |a Zhang, J.-T., Chen, J., Statistical inferences for functional data (2007) Ann. Stat., 35, pp. 1052-1079 | ||
| 506 | |2 openaire |e Política editorial | ||
| 520 | 3 | |a When dealing with multivariate data robust principal component analysis (PCA), like classical PCA, searches for directions with maximal dispersion of the data projected on it. Instead of using the variance as a measure of dispersion, a robust scale estimator sn may be used in the maximization problem. In this paper, we review some of the proposed approaches to robust functional PCA including one which adapts the projection pursuit approach to the functional data setting. © 2014, Springer International Publishing Switzerland. |l eng | |
| 536 | |a Detalles de la financiación: Universidad de Buenos Aires, PIP 216 | ||
| 536 | |a Detalles de la financiación: Agencia Nacional de Promoción Científica y Tecnológica | ||
| 536 | |a Detalles de la financiación: Consejo Nacional de Investigaciones Científicas y Técnicas, PICT 821 | ||
| 536 | |a Detalles de la financiación: Acknowledgements This research was partially supported by Grants 276 from the Universidad de Buenos Aires, PIP 216 from CONICET and PICT 821 from ANPCYT at Buenos Aires, Argentina. The authors wish to thank three anonymous referees for valuable comments which led to an improved version of the original paper. | ||
| 593 | |a Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires and CONICET, Buenos Aires, Argentina | ||
| 690 | 1 | 0 | |a COVARIANCE OPERATOR |
| 690 | 1 | 0 | |a FUNCTIONAL DATA ANALYSIS |
| 690 | 1 | 0 | |a PRINCIPAL DIRECTION |
| 690 | 1 | 0 | |a ROBUST ESTIMATOR |
| 690 | 1 | 0 | |a SCHMIDT OPERATOR |
| 700 | 1 | |a Boente, G. | |
| 773 | 0 | |d Springer International Publishing, 2014 |h pp. 41-54 |p Stud. Theo. Appl. Stat. Sel. Papers Stat. Soc. |x 21947767 |t Studies in Theoretical and Applied Statistics, Selected Papers of the Statistical Societies | |
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