# Histogram selection in non Gaussian regression

ESAIM: Probability and Statistics (2009)

- Volume: 13, page 70-86
- ISSN: 1292-8100

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topSauvé, Marie. "Histogram selection in non Gaussian regression." ESAIM: Probability and Statistics 13 (2009): 70-86. <http://eudml.org/doc/250623>.

@article{Sauvé2009,

abstract = {
We deal with the problem of choosing a piecewise constant estimator of a regression function s mapping $\mathcal\{X\}$ into $\mathbb\{R\}$.
We consider a non Gaussian regression framework with deterministic design points, and we adopt the non asymptotic approach of model selection via penalization developed by Birgé and Massart.
Given a collection of partitions of $\mathcal\{X\}$,
with possibly exponential complexity,
and the corresponding collection of piecewise constant estimators,
we propose a penalized least squares criterion which selects a partition whose associated estimator performs approximately as well as the best one,
in the sense that its quadratic risk is close to the infimum of the risks.
The risk bound we provide is non asymptotic.
},

author = {Sauvé, Marie},

journal = {ESAIM: Probability and Statistics},

keywords = {CART; change-points detection; deviation inequalities; model selection; oracle inequalities; regression; change-point detection},

language = {eng},

month = {3},

pages = {70-86},

publisher = {EDP Sciences},

title = {Histogram selection in non Gaussian regression},

url = {http://eudml.org/doc/250623},

volume = {13},

year = {2009},

}

TY - JOUR

AU - Sauvé, Marie

TI - Histogram selection in non Gaussian regression

JO - ESAIM: Probability and Statistics

DA - 2009/3//

PB - EDP Sciences

VL - 13

SP - 70

EP - 86

AB -
We deal with the problem of choosing a piecewise constant estimator of a regression function s mapping $\mathcal{X}$ into $\mathbb{R}$.
We consider a non Gaussian regression framework with deterministic design points, and we adopt the non asymptotic approach of model selection via penalization developed by Birgé and Massart.
Given a collection of partitions of $\mathcal{X}$,
with possibly exponential complexity,
and the corresponding collection of piecewise constant estimators,
we propose a penalized least squares criterion which selects a partition whose associated estimator performs approximately as well as the best one,
in the sense that its quadratic risk is close to the infimum of the risks.
The risk bound we provide is non asymptotic.

LA - eng

KW - CART; change-points detection; deviation inequalities; model selection; oracle inequalities; regression; change-point detection

UR - http://eudml.org/doc/250623

ER -

## References

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