Hello everyone. I have got a little question on selecting a proper bandwidth for kernel regression.
As you all know, for bandwidth selection in a regression case you can use the averaged squared error as a criterion for goodness of fit, but for some problems (e.g. the bandwidth h approaches to zero), it is better to use the cross-validation criterion in combination with some penalty function (Generalized Cross-Validation, Shibata's Model Selector, Akaike's Information Criterion, Rice's T,...). So my problem: Has got anyone any idea or a tip, where I can find some existing R-functions, that satisfy my needs? Or has anyone any idea how such a function would look like, for example to compute a bandwidth selection with the (Generalized) Cross-Validation or Rice's T, as I mentioned above? My aim is to fit in a kernel regression to a time series with the function ksmooth(), but there is still the bandwidth to select. Have a nice weekend and thanks a lot. Sincerely Andreas Klein. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
