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We propose a feature selection method for density estimation with
quadratic loss. This method relies on the study of unidimensional
approximation models and on the definition of confidence regions for
the density thanks to these models. It is quite general and includes
cases of interest like detection of relevant wavelets coefficients
or selection of support vectors in SVM. In the general case, we
prove that every selected feature actually improves the performance
of the estimator. In the case...
Although many words have been written about two recent directional (regression) quantile concepts, their applications, and the algorithms for computing associated (regression) quantile regions, their software implementation is still not widely available, which, of course, severely hinders the dissemination of both methods. Wanting to partly fill in the gap here, we provide all the codes needed for computing and plotting the multivariate (regression) quantile regions in Octave and MATLAB, describe...
Recently, the eminently popular standard quantile regression has been generalized to the multiple-output regression setup by means of directional regression quantiles in two rather interrelated ways. Unfortunately, they lead to complicated optimization problems involving parametric programming, and this may be the main obstacle standing in the way of their wide dissemination. The presented R package modQR is intended to address this issue. It originates as a quite faithful translation of the authors'...
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