# Partition-based conditional density estimation

ESAIM: Probability and Statistics (2013)

- Volume: 17, page 672-697
- ISSN: 1292-8100

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topCohen, S. X., and Le Pennec, E.. "Partition-based conditional density estimation." ESAIM: Probability and Statistics 17 (2013): 672-697. <http://eudml.org/doc/274371>.

@article{Cohen2013,

abstract = {We propose a general partition-based strategy to estimate conditional density with candidate densities that are piecewise constant with respect to the covariate. Capitalizing on a general penalized maximum likelihood model selection result, we prove, on two specific examples, that the penalty of each model can be chosen roughly proportional to its dimension. We first study a classical strategy in which the densities are chosen piecewise conditional according to the variable. We then consider Gaussian mixture models with mixing proportion that vary according to the covariate but with common mixture components. This model proves to be interesting for an unsupervised segmentation application that was our original motivation for this work.},

author = {Cohen, S. X., Le Pennec, E.},

journal = {ESAIM: Probability and Statistics},

keywords = {conditional density estimation; partition; penalized likelihood; piecewise polynomial density; gaussian mixture model; piecewise polynomial densities; Gaussian mixture models},

language = {eng},

pages = {672-697},

publisher = {EDP-Sciences},

title = {Partition-based conditional density estimation},

url = {http://eudml.org/doc/274371},

volume = {17},

year = {2013},

}

TY - JOUR

AU - Cohen, S. X.

AU - Le Pennec, E.

TI - Partition-based conditional density estimation

JO - ESAIM: Probability and Statistics

PY - 2013

PB - EDP-Sciences

VL - 17

SP - 672

EP - 697

AB - We propose a general partition-based strategy to estimate conditional density with candidate densities that are piecewise constant with respect to the covariate. Capitalizing on a general penalized maximum likelihood model selection result, we prove, on two specific examples, that the penalty of each model can be chosen roughly proportional to its dimension. We first study a classical strategy in which the densities are chosen piecewise conditional according to the variable. We then consider Gaussian mixture models with mixing proportion that vary according to the covariate but with common mixture components. This model proves to be interesting for an unsupervised segmentation application that was our original motivation for this work.

LA - eng

KW - conditional density estimation; partition; penalized likelihood; piecewise polynomial density; gaussian mixture model; piecewise polynomial densities; Gaussian mixture models

UR - http://eudml.org/doc/274371

ER -

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