Data-driven bandwidth choice for gamma kernel estimates of density derivatives on the positive semi-axis

Autor:	Dobrovidov, A. V., Markovich, L. A
Rok vydání:	2014
Předmět:	Mathematics - Probability Mathematics - Statistics Theory
Druh dokumentu:	Working Paper
Popis:	In some applications it is necessary to estimate derivatives of probability densities defined on the positive semi-axis. The quality of nonparametric estimates of the probability densities and their derivatives are strongly influenced by smoothing parameters (bandwidths). In this paper an expression for the optimal smoothing parameter of the gamma kernel estimate of the density derivative is obtained. For this parameter data-driven estimates based on methods called "rule of thumb" and "cross-validation" are constructed. The quality of the estimates is verified and demonstrated on examples of density derivatives generated by Maxwell and Weibull distributions. Comment: 5 pages, 4 figures, Proc. of IFAC International Workshop on Adaptation and Learning in Control and Signal Processing,pp. 500-505, 2013, Caen, France, July 3-5
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1401.6801 Zobrazit plný text záznamu View this record from Arxiv