Department of Pattern Recognition

(eng): We extend the concept of piecewise linear histogram introduced recently by Beirlant, Berlinet and Gyorfi. The disadvantage of that histogram is that in many models it takes on negative values with probability close to 1. We show that for a wide set of models, the extended class of estimates contains a bona fide density with probability tending to 1 as the sample size n increases to infinity, with the error decreasing with the same rate as in the original narrower class.