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@inproceedings{561831, author = {Koláček, Jan}, address = {Brno}, booktitle = {Datastat 01, Folia Fac. Sci. Nat. Univ. Masaryk. Brunensis, Mathematica 11}, keywords = {Regression function; kernel smoothing; bandwidth}, howpublished = {tištěná verze "print"}, language = {eng}, location = {Brno}, isbn = {80-210-3028-3}, pages = {129-138}, publisher = {Masaryk University}, title = {Kernel Estimation of the Regression Function - Bandwidth Selection}, year = {2002} }
TY - JOUR ID - 561831 AU - Koláček, Jan PY - 2002 TI - Kernel Estimation of the Regression Function - Bandwidth Selection PB - Masaryk University CY - Brno SN - 8021030283 KW - Regression function KW - kernel smoothing KW - bandwidth N2 - The problem of deciding how much to smooth is of great importance in nonparametric regression. Before embarking on technical solutions of the problem it is worth noting that a selection of the smoothing parameter is always related to a certain interpretation of the smooth. However, a good automatically selected parameter is always a useful starting (view)point. An advantage of automatic selection of the bandwidth for kernel smoothers is that comparison between laboratories can be made on the basis of a standardized method. Various methods for choosing the smoothing parameter are presented in the following sections. The choice is made so that some global error criterion is minimized. This paper shortly aspires to summarize attained results from this branch and to demonstrate their application for simulated data sets. ER -
KOLÁČEK, Jan. Kernel Estimation of the Regression Function - Bandwidth Selection. In \textit{Datastat 01, Folia Fac. Sci. Nat. Univ. Masaryk. Brunensis, Mathematica 11}. Brno: Masaryk University, 2002, s.~129-138. ISBN~80-210-3028-3.
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