- Issue
- Journal of Siberian Federal University. Mathematics & Physics. 2023 16 (2)
- Authors
- Ryma Douas; Ilhem Laroussi; Soumia Kharfouchi
- Contact information
- Ryma Douas: Sciences Laboratory Department of Mathematics Mentouri Brothers University Constantine, Algeria; ; Ilhem Laroussi: Sciences Laboratory Department of Mathematics Mentouri Brothers University Constantine, Algeria; ; Soumia Kharfouchi: Department of Mathematics Laboratory of Mathematical Biostatistics Bioinformatics and Methodology Applied to Health Sciences Mentouri Brothers University Constantine, Algeria;
- Keywords
- non-parametric regression; L2 error; least squares estimators; orthogonal series estimates; convergence in the L2-norm; twice censored data; regression estimation; hard thresholding
- Abstract
In this paper, we introduce an estimator of the least squares regression function, for Y right censored by R and min(Y;R) left censored by L. It is based on ideas derived from the context of wavelet estimates and is constructed by rigid thresholding of the coefficient estimates of a series development of the regression function. We establish convergence in norm L2. We give enough criteria for the consistency of this estimator. The result shows that our estimator is able to adapt to the local regularity of the related regression function and distribution
- Pages
- 204–215
- EDN
- GVQDEI
- Paper at repository of SibFU
- https://elib.sfu-kras.ru/handle/2311/149916
Journal of Siberian Federal University. Mathematics & Physics / Incomplete Least Squared Regression Function Estimator Based on Wavelets
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