deconf: Classical deconvolution density estimate

In DeconWK: Deconvolution by Weighted Kernels

Description

Calculates the classical deconvolution density estimate given in equation (4) of Hazelton and Turlach (2009).

Usage

decon.f(y, eval = NA, h = NA, sigma)

Arguments

y	the observed values.
eval	grid on which the deconvolution density estimate be calculated.
h	the smoothing parameter to be used.
sigma	the standard deviation of the contaminating (normal) distribution.

Details

If "eval" is not specified, it defaults to seq(min(y)-sd(y), max(y)+sd(y), length=100).

If "h" is not specified, the plug-in bandwidth selector developed by Delaigle and Gijbels (2004) is used.

Value

A matrix with two columns named "x" and "y"; the first column contains the evaluation grid, "eval", and the second column the deconvolution density estimate.

Author(s)

Martin L Hazelton [e-mail]

References

Delaigle, A. and Gijbels, I. (2004). Practical bandwidth selection in deconvolution kernel density estimation. Computational Statistics & Data Analysis 45(2): 249–267.

Hazelton, M.L. and Turlach, B.A. (2009). Nonparametric density deconvolution by weighted kernel estimators, Statistics and Computing 19(3): 217–228. http://dx.doi.org/10.1007/s11222-008-9086-7.

Examples

  set.seed(100719)
  y <- rden(100, DEN=3, sigma=sqrt(29/40)) # Var(Z)/Var(X) = 0.1
  f.hat <- decon.f(y, sigma=sqrt(29/40))
  plot(f.hat, type="l")
  abline(h=0)

DeconWK documentation built on May 2, 2019, 6:08 p.m.