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
IW_p(B_0, \nu_0)
\Sigma \mid X_1, \ldots, X_n \sim IW_p(B_0 + nS_n, \nu_0 + n),
where
S_n = n^{-1}\sum_{i=1}^{n}X_iX_i^\top
.Post-processing step: Post-process the samples generated from the initial samples
\Sigma_{(i)} := \left\{\begin{array}{ll}H_{\gamma_n}(\Sigma^{(i)}) + \left[\epsilon_n - \lambda_{\min}\{H_{\gamma_n}(\Sigma^{(i)})\}\right]I_p, & \mbox{ if } \lambda_{\min}\{H_{\gamma_n}(\Sigma^{(i)})\} < \epsilon_n, \\ H_{\gamma_n}(\Sigma^{(i)}), & \mbox{ otherwise }, \end{array}\right.
where \Sigma^{(1)}, \ldots, \Sigma^{(N)}
are the initial posterior samples,
\epsilon_n
is a positive real number, and H_{\gamma_n}(\Sigma)
denotes the generalized threshodling operator given as
(H_{\gamma_n}(\Sigma))_{ij} = \left\{\begin{array}{ll}\sigma_{ij}, & \mbox{ if } i = j, \\
h_{\gamma_n}(\sigma_{ij}), & \mbox{ if } i \neq j, \end{array}\right.
where \sigma_{ij}
is the (i,j)
element of \Sigma
and h_{\gamma_n}(\cdot)
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)