We investigate the optimality for model selection of the so-called slope heuristics,
Accepté le :
DOI : 10.1051/ps/2017005
Mots-clés : Nonparametric regression, heteroscedastic noise, random design, model selection, cross-validation, wavelets
@article{PS_2017__21__412_0, author = {Navarro, Fabien and Saumard, Adrien}, title = {Slope heuristics and {V-Fold} model selection in heteroscedastic regression using strongly localized bases}, journal = {ESAIM: Probability and Statistics}, pages = {412--451}, publisher = {EDP-Sciences}, volume = {21}, year = {2017}, doi = {10.1051/ps/2017005}, mrnumber = {3743921}, zbl = {1395.62093}, language = {en}, url = {https://www.numdam.org/articles/10.1051/ps/2017005/} }
TY - JOUR AU - Navarro, Fabien AU - Saumard, Adrien TI - Slope heuristics and V-Fold model selection in heteroscedastic regression using strongly localized bases JO - ESAIM: Probability and Statistics PY - 2017 SP - 412 EP - 451 VL - 21 PB - EDP-Sciences UR - https://www.numdam.org/articles/10.1051/ps/2017005/ DO - 10.1051/ps/2017005 LA - en ID - PS_2017__21__412_0 ER -
%0 Journal Article %A Navarro, Fabien %A Saumard, Adrien %T Slope heuristics and V-Fold model selection in heteroscedastic regression using strongly localized bases %J ESAIM: Probability and Statistics %D 2017 %P 412-451 %V 21 %I EDP-Sciences %U https://www.numdam.org/articles/10.1051/ps/2017005/ %R 10.1051/ps/2017005 %G en %F PS_2017__21__412_0
Navarro, Fabien; Saumard, Adrien. Slope heuristics and V-Fold model selection in heteroscedastic regression using strongly localized bases. ESAIM: Probability and Statistics, Tome 21 (2017), pp. 412-451. doi : 10.1051/ps/2017005. https://www.numdam.org/articles/10.1051/ps/2017005/
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