# 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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