asus | R Documentation |
ASUS procedure for shrinkage estimation of a high dimensional sparse parameter.
asus(d, v.d, s, k = 2, m = 50)
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 |
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.
est - an n vector holding the estimates
mse - estimate of risk
tau - k-1 vector of grouping parameters if k>=2
t - k vector of thresholding parameters
size - k vector of group sizes
Banerjee. T, Mukherjee. G and Sun. W. Adaptive Sparse Estimation with Side Information. Journal of the American Statistical Association 115, no. 532 (2020): 2053-2067.
sureshrink
,ejs
,sureshrink.mse
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)
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