asus (version 1.0.0)

asus: Adaptive SURE thresholding with side information (asus)

Description

ASUS procedure for shrinkage estimation of a high dimensional sparse parameter.

Usage

asus(d, v.d, s, k = 2, m = 50)

Arguments

d

an n vector of primary observations

v.d

an n vector of variances for each component of d

s

an n vector of side information

k

number of groups. Default is k=2

m

partitions the support of \(|s|\) into \(m\) equidistant points. Default is \(m=50\)

Value

1. est - an n vector holding the estimates

2. mse - estimate of risk

3. tau - k-1 vector of grouping parameters if k>=2

4. t - k vector of thresholding parameters

5. size - k vector of group sizes

Details

Estimates a sparse high dimensional vector using the ASUS procedure described in Banerjee et al. (2017). If k = 1 then ASUS is the SureShrink estimator. The current implementation of ASUS estimates the grouping thresholds based on the magnitude of \(|s|\). See the reference for more details.

References

Banerjee. T, Mukherjee. G and Sun. W (2017). Adaptive Sparse Estimation with Side Information.

See Also

sureshrink,ejs,sureshrink.mse

Examples

# NOT RUN {
library(asus)
set.seed(42)
d<-rnorm(10,2,1)
v.d<- rep(1,10)
set.seed(42)
s<-rnorm(10,3,0.1)
asus.out<-asus(d,v.d,s)

# }