ST

0th

Percentile

Testing for Sparse Signals

This function implements the method for testing sparse signals in Zhang and Cheng (2017).

Usage
ST(X.f, Y.f, sub.size, test.set, M = 500, alpha = 0.05)
Arguments
X.f

n times p design matrix.

Y.f

Response variable.

sub.size

The sub-sample size used for screening.

test.set

The set of variables of interest.

M

The number of bootstrap replications (default 500).

alpha

The nominal level alpha (default 0.05).

Value

Values of the non-studentized and studentized statistics, and whether the tests get rejected at the level alpha.

References

Zhang, X., and Cheng, G. (2017) Simultaneous Inference for High-dimensional Linear Models, Journal of the American Statistical Association, 112, 757-768.

Aliases
  • ST
Examples
# NOT RUN {
## The function is intended for large n and p.
## Use small p here for illustration purpose only.
n <- 100
p <- 10
s0 <- 3
set <- 1:s0
Sigma <- matrix(NA, p, p)
for (i in 1:p) Sigma[i,] <- 0.9^(abs(i-(1:p)))
X <- matrix(rnorm(n*p), n, p)
X <- t(t(chol(Sigma))%*%t(X))
beta <- rep(0,p)
beta[1:s0] <- runif(s0,0,2)
Y <- X%*%beta+rt(n,4)/sqrt(2)
test.set <- (s0+1):p
sub.size <- n*0.3
ST(X, Y, sub.size, test.set)
test.set <- s0:p
ST(X, Y, sub.size, test.set)
# }
Documentation reproduced from package SILM, version 1.0.0, License: GPL-3

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