NON COMPACT ESTIMATION OF THE CONDITIONAL DENSITY FROM DIRECT OR NOISY DATA

In this paper, we apply a penalized maximum likelihood model selection result of [13] to partition-based collection in which the conditional densities depend on covariate in a

In particular, the Flexcode method originally proposes to transfer successful procedures for high dimensional regression to the conditional density estimation setting by

In this paper, we discuss the problem of adaptive confidence balls, from a non-asymptotic point of view, in the particular context of density

The traditional approach to estimating RBMs is feature-based, for the main reason that when landmarks are used as centres, then the coefficients of the transformation can be computed

In this paper we propose an alternative solution to the above problem of estimating a general conditional density function of tar- gets given inputs, in the form of a Gaussian

After a general introduction to these partition-based strategies, we study two cases: a classical one in which the conditional density depends in a piecewise polynomial manner of

The second method originally proposes to convert successful high dimensional regression methods into the conditional density estimation, interpreting the coefficients of the

Here, a probabilistic neural network-based downscaling method is used to predict the probability distribution of precipitation at a study site conditional upon the synoptic state of