Article ID Journal Published Year Pages File Type
1149366 Journal of Statistical Planning and Inference 2011 11 Pages PDF
Abstract

In this paper, we are interested in the study of beta kernel density estimators from an asymptotic minimax point of view. These estimators allows to estimate density functions with support in [0,1]. It is well-known that beta kernel estimators are – on the contrary of classical kernel estimators – “free of boundary effect” and thus are very useful in practice. The goal of this paper is to prove that there is a price to pay: for very regular density functions or for certain losses, these estimators are not minimax. Nevertheless they are minimax for classical regularities such as regularity of order two or less than two, supposed commonly in the practice and for some classical losses.

Related Topics
Physical Sciences and Engineering Mathematics Applied Mathematics
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