Data-Driven Bandwidth Choice for Density Estimation Based on Dependent Data
Hart, Jeffrey D. ; Vieu, Philippe
Ann. Statist., Tome 18 (1990) no. 1, p. 873-890 / Harvested from Project Euclid
Text on Project Euclid
PDF
Alt PDF
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.
Publié le : 1990-06-14
Classification:  Nonparametric density estimation,  kernel estimate,  bandwidth selection,  $\alpha$-mixing processes,  cross-validation,  65G05,  62G20,  62M99,  62M10,  60G10,  60G35 
@article{1176347630,
     author = {Hart, Jeffrey D. and Vieu, Philippe},
     title = {Data-Driven Bandwidth Choice for Density Estimation Based on Dependent Data},
     journal = {Ann. Statist.},
     volume = {18},
     number = {1},
     year = {1990},
     pages = { 873-890},
     language = {en},
     url = {http://dml.mathdoc.fr/item/1176347630}
}

Hart, Jeffrey D.; Vieu, Philippe. Data-Driven Bandwidth Choice for Density Estimation Based on Dependent Data. Ann. Statist., Tome 18 (1990) no. 1, pp.  873-890. http://gdmltest.u-ga.fr/item/1176347630/