FacRowMinST {TOSI} | R Documentation |
Data splitting-based two-stage minimum testing method for a group of loading vectors in factor models.
Description
Conduct the simultaneous inference for a set of loading vectors inr the NUll hypothesises H02 that assumes there is zero loading vector in the set of loading vectors.
Usage
FacRowMinST(X, G2, q=NULL, Nsplit= 5, sub.frac=0.5,
alpha=0.05, standardized=FALSE,seed=1)
Arguments
X |
a |
G2 |
a positive vector with values between 1 and p, the set of H02. |
q |
a positive integer, the number of factors. It will automatically selected by a criterion if it is NULL. |
Nsplit |
a positive integer, the number of data spliting, default as 5. |
sub.frac |
a positive number between 0 and 1, the proportion of the sample used in stage I. |
alpha |
a positive real, the significance level. |
standardized |
a logical value, whether use the standardized test statistic. |
seed |
a non-negative integer, the random seed. |
Value
return a vector with names 'CriticalValue', 'TestStatistic', 'reject_status', 'p-value' if Nsplit=1, and 'reject_status' and 'adjusted_p-value' if Nsplit>1.
Note
nothing
Author(s)
Liu Wei
References
Wei Liu, Huazhen Lin, Jin Liu (2020). Estimation and inference on high-dimensional sparse factor models.
See Also
Examples
### Example
dat <- gendata_Fac(n = 300, p = 500)
res <- Factorm(dat$X)
X <- dat$X
# ex1: H01 is false
G2 <- 1:200; # all are nonzero loading vectors
FacRowMinST(X, G2=G2, alpha=0.05, sub.frac=0.5)
FacRowMinST(X, q= 6, G2=G2, alpha=0.05, sub.frac=0.5) # specify the true number of factors
# ex2: H01 is true
G2 <- 1:500 # all are zero loading vectors
FacRowMinST(X, G2=G2, alpha=0.05, sub.frac=0.5)
FacRowMinST(X, q= 7, G2=G2, alpha=0.05, sub.frac=0.5) # specify a false number of factors