f.kde: Kernel Density f-Estimator
In barbehenna/coleReg: COmpound Loss Emporium with Regression
f.kde R Documentation
Kernel Density f-Estimator
Description
An implementation of the f-estimator from "Nonparametric empirical Bayes and
compound decision approaches to estimation of a high-dimensional vector of
normal means" (Brown and Greenshtein, 2009) for estimating the mean of a
homoscedastic sequence of independent Gaussian observations under squared
error loss. The various small modifications and default parameters that are
suggested in their paper are implemented.
Usage
f.kde(x, s, bw = s/sqrt(log(length(x))), Cn = s * sqrt(3 * log(length(x))))
Arguments
x
Gaussian sequence
s
standard deviation
bw
scalar bandwidth for Gaussian kernel density estimate
Cn
scalar threshold for to clip estimates in low-density areas (Inf for no thresholding)
Value
theta_hat
estimated values of means of Gaussian sequence
Examples
theta = rnorm(250)
x = theta + rnorm(250)
res = f.kde(x, s = 1)
mean((theta - res$theta_hat)^2)
barbehenna/coleReg documentation built on May 8, 2022, 12:05 a.m.
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| f.kde | R Documentation |
Kernel Density f-Estimator
Description
An implementation of the f-estimator from "Nonparametric empirical Bayes and compound decision approaches to estimation of a high-dimensional vector of normal means" (Brown and Greenshtein, 2009) for estimating the mean of a homoscedastic sequence of independent Gaussian observations under squared error loss. The various small modifications and default parameters that are suggested in their paper are implemented.
Usage
f.kde(x, s, bw = s/sqrt(log(length(x))), Cn = s * sqrt(3 * log(length(x))))
Arguments
x |
Gaussian sequence |
s |
standard deviation |
bw |
scalar bandwidth for Gaussian kernel density estimate |
Cn |
scalar threshold for to clip estimates in low-density areas (Inf for no thresholding) |
Value
theta_hat |
estimated values of means of Gaussian sequence |
Examples
theta = rnorm(250) x = theta + rnorm(250) res = f.kde(x, s = 1) mean((theta - res$theta_hat)^2)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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