This file is part of IDEAS, which uses RePEc data


[ Papers | Articles | Software | Books | Chapters | Authors | Institutions | JEL Classification | NEP reports | Search | New papers by email | Author registration | Rankings | Volunteers | FAQ | Blog | Help! ]

Depth-Based Classification For Functional Data

Author info | Abstract | Publisher info | Download info | Related research | Statistics
Author Info
Sara Lopez-Pintado ()
Juan Romo ()
Abstract

Classification is an important task when data are curves. Recently, the notion of statistical depth has been extended to deal with functional observations. In this paper, we propose robust procedures based on the concept of depth to classify curves. These techniques are applied to a real data example. An extensive simulation study with contaminated models illustrates the good robustness properties of these depth-based classification methods.

Download Info
To download:

If you experience problems downloading a file, check if you have the proper application to view it first. Information about this may be contained in the File-Format links below. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://docubib.uc3m.es/WORKINGPAPERS/WS/ws055611.pdf
File Format: application/pdf
File Function:
Download Restriction: no

Publisher Info
Paper provided by Universidad Carlos III, Departamento de Estadística y Econometría in its series Statistics and Econometrics Working Papers with number ws055611.

Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Length:
Date of creation: Oct 2005
Date of revision:
Handle: RePEc:cte:wsrepe:ws055611

Contact details of provider:
Postal: C/ Madrid, 126 - 28903 GETAFE (MADRID)
Phone: 6249847
Fax: 6249849
Web page: http://www.uc3m.es/uc3m/dpto/DEE/departamento.html
More information through EDIRC

For technical questions regarding this item, or to correct its listing, contact: ().

Related research
Keywords:

This paper has been announced in the following NEP Reports:

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. C. Abraham & P. A. Cornillon & E. Matzner-Løber & N. Molinari, 2003. "Unsupervised Curve Clustering using B-Splines," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics, Finnish Statistical Society, Norwegian Statistical Association and Swedish Statistical Association, vol. 30(3), pages 581-595. [Downloadable!] (restricted)
  2. Ferraty, F. & Vieu, P., 2003. "Curves discrimination: a nonparametric functional approach," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 161-173, October. [Downloadable!] (restricted)
  3. Gareth M. James & Trevor J. Hastie, 2001. "Functional linear discriminant analysis for irregularly sampled curves," Journal Of The Royal Statistical Society Series B, Royal Statistical Society, vol. 63(3), pages 533-550. [Downloadable!] (restricted)
  4. Jörnsten, Rebecka, 2004. "Clustering and classification based on the L1 data depth," Journal of Multivariate Analysis, Elsevier, vol. 90(1), pages 67-89, July. [Downloadable!] (restricted)
  5. James G.M. & Sugar C.A., 2003. "Clustering for Sparsely Sampled Functional Data," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 397-408, January. [Downloadable!] (restricted)
  6. Oja, Hannu, 1983. "Descriptive statistics for multivariate distributions," Statistics & Probability Letters, Elsevier, vol. 1(6), pages 327-332, October. [Downloadable!] (restricted)
  7. Ruts, Ida & Rousseeuw, Peter J., 1996. "Computing depth contours of bivariate point clouds," Computational Statistics & Data Analysis, Elsevier, vol. 23(1), pages 153-168, November. [Downloadable!] (restricted)
  8. Anil K. Ghosh & Probal Chaudhuri, 2005. "On Maximum Depth and Related Classifiers," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics, Finnish Statistical Society, Norwegian Statistical Association and Swedish Statistical Association, vol. 32(2), pages 327-350. [Downloadable!] (restricted)
Full references

Statistics
Access and download statistics

Did you know? You can include your works in the database easily by uploading them on the Munich Personal RePEc Archive (MPRA) if you do not have access to an institutional RePEc archive.

This page was last updated on 2009-12-15.


This information is provided to you by IDEAS at the Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using RePEc data on a server sponsored by the Society for Economic Dynamics.