A Comparative Study of Several Methods in Kernel Density Estimation

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Authors	ŘEZÁČ Martin
Year of publication	2004
Type	Article in Proceedings
Conference	Datastat 03, Folia Fac. Sci. Nat. Univ. Masaryk. Brunensis, Mathematica 15
MU Faculty or unit	Faculty of Science
Citation
Field	Applied statistics, operation research
Keywords	Density estimation; kernel smoothing; data-driven choice
Description	This paper is concerned with the problem of data-driven choice of smoothing parameters in density estimation. Our aim is to provide a selective review of the main methods and to compare them by means of a simulation study.
Related projects:	Functional-differential equations and mathematical-statistical models