@article{epos227,
          volume = {18},
          number = {2},
          author = {Jeffrey Hart and Philippe Vieu},
           title = {Data-Driven Bandwidth Choice for Density Estimation Based on Dependent Data},
       publisher = {Institute of Mathematical Statistics},
         journal = {The Annals of Statistics},
           pages = {873--890},
            year = {1990},
             url = {https://episodesplatform.eu/eprints/227/},
        abstract = {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.}
}