Nonparametric Estimation for Functional Data by Wavelet Threshold

Authors

  • Christophe Chesneau Université de Caen
  • Maher Kachour Ecole supérieure de commerce IDRAC
  • Bertrand Maillot Université de Caen

DOI:

https://doi.org/10.57805/revstat.v11i2.134

Keywords:

functional data, density estimation, nonparametric regression, wavelets, hard thresholding

Abstract

This paper deals with density and regression estimation problems for functional data. Using wavelet bases for Hilbert spaces of functions, we develop a new adaptive procedure based on a term-by-term selection of the wavelet coefficients estimators. We study its asymptotic performances by considering the mean integrated squared error over adapted decomposition spaces.

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Published

2013-06-24

How to Cite

Chesneau , C., Kachour , M., & Maillot , B. (2013). Nonparametric Estimation for Functional Data by Wavelet Threshold. REVSTAT-Statistical Journal, 11(2), 211–230. https://doi.org/10.57805/revstat.v11i2.134

Issue

Vol. 11 No. 2 (2013): REVSTAT-Statistical Journal

Section

Article

License

Copyright (c) 2013 REVSTAT-Statistical Journal

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.