Title :
The Kernel density estimation of nonparametric model
Author_Institution :
Coll. of Math. & Comput. Sci., Guangxi Univ. for Nat., Nanning, China
Abstract :
Four nonparametric estimates of a density function are investigated. Two model estimates are defined from a global kernel estimate, while the other two are defined from a global kernel estimate of the first derivative of the density function. We show that each of these model estimates attains the same rate of convergence as the usual sample model. Then, Monte-Carlo simulations illustrate on finite samples the utility of the method based on the local estimate of the first derivative.
Keywords :
Monte Carlo methods; estimation theory; nonparametric statistics; Monte-Carlo simulations; kernel density estimation; model estimation; nonparametric density estimation; nonparametric model; Bandwidth; Computer science; Convergence; Density functional theory; Educational institutions; Electronic mail; Kernel; Mathematical model; Mathematics; Smoothing methods; Density; Derivative Estimation; Kernel Estimate;
Conference_Titel :
Control and Decision Conference, 2009. CCDC '09. Chinese
Conference_Location :
Guilin
Print_ISBN :
978-1-4244-2722-2
Electronic_ISBN :
978-1-4244-2723-9
DOI :
10.1109/CCDC.2009.5192023
