# A non asymptotic penalized criterion for Gaussian mixture model selection

ESAIM: Probability and Statistics (2012)

- Volume: 15, page 41-68
- ISSN: 1292-8100

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topMaugis, Cathy, and Michel, Bertrand. "A non asymptotic penalized criterion for Gaussian mixture model selection." ESAIM: Probability and Statistics 15 (2012): 41-68. <http://eudml.org/doc/222454>.

@article{Maugis2012,

abstract = {
Specific Gaussian mixtures are considered to solve simultaneously
variable selection and clustering problems. A non asymptotic
penalized criterion is proposed to choose the number of mixture
components and the relevant variable subset. Because of the non
linearity of the associated Kullback-Leibler contrast on Gaussian
mixtures, a general model selection theorem for maximum likelihood
estimation proposed by [Massart Concentration inequalities and model selection
Springer, Berlin (2007).
Lectures from the 33rd Summer School on Probability Theory held in
Saint-Flour, July 6–23 (2003)]
is used to obtain
the penalty function form. This theorem requires to control the
bracketing entropy of Gaussian mixture families. The ordered and
non-ordered variable selection cases are both addressed in this
paper.
},

author = {Maugis, Cathy, Michel, Bertrand},

journal = {ESAIM: Probability and Statistics},

keywords = {Model-based clustering; variable selection; penalized
likelihood criterion; bracketing entropy; model-based clustering; penalized likelihood criterion},

language = {eng},

month = {1},

pages = {41-68},

publisher = {EDP Sciences},

title = {A non asymptotic penalized criterion for Gaussian mixture model selection},

url = {http://eudml.org/doc/222454},

volume = {15},

year = {2012},

}

TY - JOUR

AU - Maugis, Cathy

AU - Michel, Bertrand

TI - A non asymptotic penalized criterion for Gaussian mixture model selection

JO - ESAIM: Probability and Statistics

DA - 2012/1//

PB - EDP Sciences

VL - 15

SP - 41

EP - 68

AB -
Specific Gaussian mixtures are considered to solve simultaneously
variable selection and clustering problems. A non asymptotic
penalized criterion is proposed to choose the number of mixture
components and the relevant variable subset. Because of the non
linearity of the associated Kullback-Leibler contrast on Gaussian
mixtures, a general model selection theorem for maximum likelihood
estimation proposed by [Massart Concentration inequalities and model selection
Springer, Berlin (2007).
Lectures from the 33rd Summer School on Probability Theory held in
Saint-Flour, July 6–23 (2003)]
is used to obtain
the penalty function form. This theorem requires to control the
bracketing entropy of Gaussian mixture families. The ordered and
non-ordered variable selection cases are both addressed in this
paper.

LA - eng

KW - Model-based clustering; variable selection; penalized
likelihood criterion; bracketing entropy; model-based clustering; penalized likelihood criterion

UR - http://eudml.org/doc/222454

ER -

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