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Depth-Based Inference For Functional Data

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  • Sara Lopez-Pintado

    ()

  • Juan Romo

    ()

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    Abstract

    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.

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    File URL: http://docubib.uc3m.es/WORKINGPAPERS/WS/ws063113.pdf
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    Bibliographic Info

    Paper provided by Universidad Carlos III, Departamento de Estadística y Econometría in its series Statistics and Econometrics Working Papers with number ws063113.

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    Date of creation: May 2006
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    Handle: RePEc:cte:wsrepe:ws063113

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    1. 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.
    2. 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.
    3. Oja, Hannu, 1983. "Descriptive statistics for multivariate distributions," Statistics & Probability Letters, Elsevier, vol. 1(6), pages 327-332, October.
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