HPCA_FN {HDRFA} | R Documentation |
Estimating Factor Numbers via Rank Minimization Corresponding to Huber PCA
Description
This function is to estimate factor numbers via rank minimization corresponding to Huber Principal Component Analysis (HPCA).
Usage
HPCA_FN(X, rmax, Method = "E", tau = NULL, scale_est="MAD", threshold = NULL,
L_init = NULL, F_init = NULL, maxiter_HPCA = 100, maxiter_HLM = 100,
eps = 0.001)
Arguments
X |
Input matrix, of dimension |
rmax |
The user-supplied maximum factor numbers. |
Method |
|
tau |
Optional user-supplied parameter for Huber loss; default is NULL, and |
scale_est |
A parameter for the elementwise Huber loss. |
threshold |
The threshold of rank minimization; default is NULL. |
L_init |
User-supplied inital value of loadings in the HPCA; default is the PCA estimator. |
F_init |
User-supplied inital value of factors in the HPCA; default is the PCA estimator. |
maxiter_HPCA |
The maximum number of iterations in the HPCA. The default is |
maxiter_HLM |
The maximum number of iterations in the iterative Huber regression algorithm. The default is |
eps |
The stopping critetion parameter in the HPCA. The default is 1e-3. |
Details
See He et al. (2023) for details.
Value
rhat |
The estimated factor number. |
Author(s)
Yong He, Lingxiao Li, Dong Liu, Wenxin Zhou.
References
He Y, Li L, Liu D, Zhou W., 2023 Huber Principal Component Analysis for Large-dimensional Factor Models.
Examples
set.seed(1)
T=50;N=50;r=3
L=matrix(rnorm(N*r,0,1),N,r);F=matrix(rnorm(T*r,0,1),T,r)
E=matrix(rnorm(T*N,0,1),T,N)
X=F%*%t(L)+E
HPCA_FN(X,8,Method="E")
HPCA_FN(X,8,Method="P")