Analysis of kernel density estimation of functions of random variables
Abbreviated Journal Title
J. Nonparametr. Stat.
functions of random variables; density estimation; central limit; theorem; asymptotic expansion; kernel contrast; bandwidth selection; CONDITIONAL U-STATISTICS; Statistics & Probability
In the current investigation, the problem,of estimating the probability density of a function of in in dependent identically distributed random variables, g(X-1, ..., X-m) is considered. Defining the integrated square contrast (ISC)and its mean (MISC), we study the central limit theorem of (ISO-MISC) as well as the second order approximation of both ISC and MISC. Via simulation and also using real data, we address some of the practical aspects of choosing the optimal bandwidth which minimizes the asymptotic MISC and its data based analog which minimizes ISC.
Journal of Nonparametric Statistics
"Analysis of kernel density estimation of functions of random variables" (2003). Faculty Bibliography 2000s. 3586.