g_rule: Separable rule for Gaussian mean estimation

In sdzhao/cole: COmpound Loss Emporium

Separable rule for Gaussian mean estimation

Description

Given tuning parameter vector t, returns corresponding estimate for the mean vector of a homoscedastic sequence of independent Gaussian observations

Usage

g_rule(x, s, t, rho = 0)

Arguments

x	primary Gaussian sequence
s	standard deviation of primary sequence
t	tuning parameter vector t1
rho	regularization parameter, closer to 0 means less regularization

Value

estimated values of means of primary Gaussian sequence

Examples

## generate data
n = 250
set.seed(1)
theta = rnorm(n)
x = theta + rnorm(n)
## loss of MLE
mean((theta - x)^2)
## loss of oracle separable estimator
mean((theta - g_rule(x, 1, theta))^2)

sdzhao/cole documentation built on May 2, 2022, 9:42 a.m.