Plug-in bandwidth selection in kernel hazard estimation from dependent data

The plug-in bandwidth selection method in nonparametric kernel hazard estimation is considered, and a weak dependence on the sample data is assumed. A general result of asymptotic optimality for the plug-in bandwidth is presented, that is valid for the hazard function, as well as for the density and distribution functions. In a simulation study, this method is compared with the “leave more than one out” cross-validation criterion under dependence. Simulations show that smaller errors and much less sample variability can be reached, and that a good selection of the pilot bandwidth can be done by means of “leave one out” cross-validation. Finally, an application to an earthquake data set is made.