Conditional Density Estimation by Penalized Likelihood Model Selection and Applications

@inproceedings{Pennec2011ConditionalDE,
  title={Conditional Density Estimation by Penalized Likelihood Model Selection and Applications},
  author={Erwan Le Pennec},
  year={2011}
}
In this technical report, we consider conditional density estimation with a maximum likelihood approach. Under weak assumptions, we obtain a theoretical bound for a KullbackLeibler type loss for a single model maximum likelihood estimate. We use a penalized model selection technique to select a best model within a collection. We give a general condition on penalty choice that leads to oracle type inequality for the resulting estimate. This construction is applied to two examples of partition… CONTINUE READING