Depth-Based Inference For Functional Data
We propose robust inference tools for functional data based on the notion of depth for curves. We extend the ideas of trimmed regions, contours and central regions to functions and study their structural properties and asymptotic behavior. Next, we introduce a scale curve to describe dispersion in a sample of functions. The computational burden of these techniques is not heavy and so they are also adequate to analyze high-dimensional data. All these inferential methods are applied to different real data sets.
|Date of creation:||May 2006|
|Date of revision:|
|Contact details of provider:|| Postal: |
Web page: http://portal.uc3m.es/portal/page/portal/dpto_estadistica
More information through EDIRC
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.:
- Oja, Hannu, 1983. "Descriptive statistics for multivariate distributions," Statistics & Probability Letters, Elsevier, vol. 1(6), pages 327-332, October.
- Sara Lopez-Pintado & Juan Romo, 2006. "On The Concept Of Depth For Functional Data," Statistics and Econometrics Working Papers ws063012, Universidad Carlos III, Departamento de Estadística y Econometría.
- Ricardo Fraiman & Graciela Muniz, 2001. "Trimmed means for functional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 10(2), pages 419-440, December.
When requesting a correction, please mention this item's handle: RePEc:cte:wsrepe:ws063113. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ()
If references are entirely missing, you can add them using this form.