Random thresholds for linear model selection
ESAIM: Probability and Statistics (2008)
- Volume: 12, page 173-195
- ISSN: 1292-8100
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topLavielle, Marc, and Ludeña, Carenne. "Random thresholds for linear model selection." ESAIM: Probability and Statistics 12 (2008): 173-195. <http://eudml.org/doc/250386>.
@article{Lavielle2008,
abstract = {
A method is introduced to select the significant or non null mean terms among a collection
of independent random variables. As an application we consider the problem of
recovering the
significant coefficients in non ordered model selection. The method is based on a convenient random centering of
the partial sums of the ordered observations. Based on
L-statistics methods we show consistency of the proposed
estimator.
An extension to unknown parametric distributions is considered.
Simulated
examples are included to show the accuracy of the estimator.
An example of signal denoising with wavelet thresholding is also discussed.
},
author = {Lavielle, Marc, Ludeña, Carenne},
journal = {ESAIM: Probability and Statistics},
keywords = {Adaptive estimation; linear model
selection; hard thresholding; random thresholding; L-statistics; adaptive estimation; linear model selection; -statistics},
language = {eng},
month = {1},
pages = {173-195},
publisher = {EDP Sciences},
title = {Random thresholds for linear model selection},
url = {http://eudml.org/doc/250386},
volume = {12},
year = {2008},
}
TY - JOUR
AU - Lavielle, Marc
AU - Ludeña, Carenne
TI - Random thresholds for linear model selection
JO - ESAIM: Probability and Statistics
DA - 2008/1//
PB - EDP Sciences
VL - 12
SP - 173
EP - 195
AB -
A method is introduced to select the significant or non null mean terms among a collection
of independent random variables. As an application we consider the problem of
recovering the
significant coefficients in non ordered model selection. The method is based on a convenient random centering of
the partial sums of the ordered observations. Based on
L-statistics methods we show consistency of the proposed
estimator.
An extension to unknown parametric distributions is considered.
Simulated
examples are included to show the accuracy of the estimator.
An example of signal denoising with wavelet thresholding is also discussed.
LA - eng
KW - Adaptive estimation; linear model
selection; hard thresholding; random thresholding; L-statistics; adaptive estimation; linear model selection; -statistics
UR - http://eudml.org/doc/250386
ER -
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