Analysis Of Kernel Density Estimation Of Functions Of Random Variables
Asymptotic expansion; Bandwidth selection; Central limit theorem; Density estimation; Functions of random variables; Kernel contrast
In the current investigation, the problem of estimating the probability density of a function of m independent identically distributed random variables, g(X1..... Xm) 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
Number of Pages
Source API URL
Ahmad, Ibrahim A. and Mugdadi, A. R., "Analysis Of Kernel Density Estimation Of Functions Of Random Variables" (2003). Scopus Export 2000s. 1649.