We wish to estimate conditional density using Gaussian Mixture Regression
model with logistic weights and means depending on the covariate. We aim at
selecting the number of components of this model as well as the other
parameters by a penalized maximum likelihood approach. We provide a lower bound
on penalty, proportional up to a logarithmic term to the dimension of each
model, that ensures an oracle inequality for our estimator. Our theoretical
analysis is supported by some numerical experiments
