thresPPP {bspcov} | R Documentation |
Bayesian Estimation of a Sparse Covariance Matrix
Description
Provides a post-processed posterior (PPP) for Bayesian inference of a sparse covariance matrix.
Usage
thresPPP(X, eps, thres = list(), prior = list(), nsample = 2000)
Arguments
X |
a n |
eps |
a small positive number decreasing to |
thres |
a list giving the information for thresholding PPP procedure.
The list includes the following parameters (with default values in parentheses):
|
prior |
a list giving the prior information.
The list includes the following parameters (with default values in parentheses):
|
nsample |
a scalar value giving the number of the post-processed posterior samples. |
Details
Lee and Lee (2023) proposed a two-step procedure generating samples from the post-processed posterior for Bayesian inference of a sparse covariance matrix:
Initial posterior computing step: Generate random samples from the following initial posterior obtained by using the inverse-Wishart prior
where
.
Post-processing step: Post-process the samples generated from the initial samples
where are the initial posterior samples,
is a positive real number, and
denotes the generalized threshodling operator given as
where is the
element of
and
is a generalized thresholding function.
For more details, see Lee and Lee (2023).
Value
Sigma |
a nsample |
p |
dimension of covariance matrix. |
Author(s)
Kwangmin Lee
References
Lee, K. and Lee, J. (2023), "Post-processes posteriors for sparse covariances", Journal of Econometrics.
See Also
cv.thresPPP
Examples
n <- 25
p <- 50
Sigma0 <- diag(1, p)
X <- MASS::mvrnorm(n = n, mu = rep(0, p), Sigma = Sigma0)
res <- bspcov::thresPPP(X, eps=0.01, thres=list(value=0.5,fun='hard'), nsample=100)
est <- bspcov::estimate(res)