%0 Journal Article
%@ 0090-5364
%A Hart, Jeffrey
%A Vieu, Philippe
%A Texas A&M University,
%A Iniversite Paul Sabatier,
%D 1990
%F epos:227
%I Institute of Mathematical Statistics
%J The Annals of Statistics
%N 2
%P 873-890
%T Data-Driven Bandwidth Choice for Density Estimation Based on Dependent Data
%U https://episodesplatform.eu/eprints/227/
%V 18
%X The bandwidth selection problem in kernel density estimation is investigated in situations where the observed data are dependent. The classical leave-out technique is extended, and thereby a class of cross-validated bandwidths is defined. These bandwidths are shown to be asymptotically optimal under a strong mixing condition. The leave-one out, or ordinary, form of cross-validation remains asymptotically optimal under the dependence model considered. However, a simulation study shows that when the data are strongly enough correlated, the ordinary version of cross-validation can be improved upon in finite-sized samples.