TY  - JOUR
ID  - epos227
UR  - http://dx.doi.org/10.1214/aos/1176347630
IS  - 2
A1  - Hart, Jeffrey
A1  - Vieu, Philippe
Y1  - 1990///
N2  - 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.
PB  - Institute of Mathematical Statistics
JF  - The Annals of Statistics
VL  - 18
SN  - 0090-5364
TI  - Data-Driven Bandwidth Choice for Density Estimation Based on Dependent Data
SP  - 873
AV  - none
EP  - 890
ER  -