DocumentCode
2785149
Title
The Kernel density estimation of nonparametric model
Author
Nong, Jifu
Author_Institution
Coll. of Math. & Comput. Sci., Guangxi Univ. for Nat., Nanning, China
fYear
2009
fDate
17-19 June 2009
Firstpage
1173
Lastpage
1177
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;
fLanguage
English
Publisher
ieee
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
Type
conf
DOI
10.1109/CCDC.2009.5192023
Filename
5192023
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