PRDenEstC: Parzen-Rosenblatt denstiy estimator.

In locpol: Kernel Local Polynomial Regression

Parzen–Rosenblatt denstiy estimator.

Description

Parzen–Rosenblat univariate density estimator.

Usage

PRDenEstC(x, xeval, bw, kernel, weig = rep(1, length(x)))

Arguments

x	vector with data points.
xeval	Vector of evaluation points.
bw	Smoothing parameter, bandwidth.
kernel	Kernel used to perform the estimation, see Kernels
weig	Vector of weights for observations.

Details

Simple Parzen–Rosenblat univariate density estimation, computed using definition.

Value

Returns an (x,den) data frame.

x	Evaluation points.
den	Density at each x point.

Author(s)

Jorge Luis Ojeda Cabrera.

References

Fan, J. and Gijbels, I. Local polynomial modelling and its applications\/. Chapman & Hall, London (1996).

Wand, M.~P. and Jones, M.~C. Kernel smoothing\/. Chapman and Hall Ltd., London (1995).

See Also

density, that uses FT to compute a kernel density estimator, bkde from package KernSmooth for a binned version, and bw.nrd0, dpik, denCVBwSelC for bandwidth selection.

Examples

	N <- 100
	x <-  runif(N)
	xeval <- 0:10/10
	b0.125 <- PRDenEstC(x, xeval, 0.125, EpaK)
	b0.05 <- PRDenEstC(x, xeval, 0.05, EpaK)
	cbind(x = xeval, fx = 1, b0.125 = b0.125$den, b0.05 = b0.05$den)

locpol documentation built on Nov. 29, 2022, 9:05 a.m.