Optimal Bandwidth Selection in Nonparametric Regression Function Estimation
Hardle, Wolfgang ; Marron, James Stephen
Ann. Statist., Tome 13 (1985) no. 1, p. 1465-1481 / Harvested from Project Euclid
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Kernel estimators of an unknown multivariate regression function are investigated. A bandwidth-selection rule is considered, which can be formulated in terms of cross validation. Under mild assumptions on the kernel and the unknown regression function, it is seen that this rule is asymptotically optimal.
Publié le : 1985-12-14
Classification:  Nonparametric regression estimation,  kernel estimators,  optimal bandwidth,  smoothing parameter,  cross validation,  62G05,  62G20 
@article{1176349748,
     author = {Hardle, Wolfgang and Marron, James Stephen},
     title = {Optimal Bandwidth Selection in Nonparametric Regression Function Estimation},
     journal = {Ann. Statist.},
     volume = {13},
     number = {1},
     year = {1985},
     pages = { 1465-1481},
     language = {en},
     url = {http://dml.mathdoc.fr/item/1176349748}
}

Hardle, Wolfgang; Marron, James Stephen. Optimal Bandwidth Selection in Nonparametric Regression Function Estimation. Ann. Statist., Tome 13 (1985) no. 1, pp.  1465-1481. http://gdmltest.u-ga.fr/item/1176349748/