PreEst.2014Banerjee: Bayesian Estimation of a Banded Precision Matrix (Banerjee...

In CovTools: Statistical Tools for Covariance Analysis

Description

PreEst.2014Banerjee returns a Bayes estimator of the banded precision matrix using G-Wishart prior. Stein’s loss or squared error loss function is used depending on the “loss” argument in the function. The bandwidth is set at the mode of marginal posterior for the bandwidth parameter.

Usage

PreEst.2014Banerjee(
  X,
  upperK = floor(ncol(X)/2),
  delta = 10,
  logpi = function(k) {     -k^4 },
  loss = c("Stein", "Squared")
)

Arguments

X	an (n\times p) data matrix where each row is an observation.
upperK	upper bound of bandwidth k.
delta	hyperparameter for G-Wishart prior. Default value is 10. It has to be larger than 2.
logpi	log of prior distribution for bandwidth k. Default is a function proportional to -k^4.
loss	type of loss; either "Stein" or "Squared".

Value

a named list containing:

C

a (p\times p) MAP estimate for precision matrix.

References

\insertRef

banerjee_posterior_2014CovTools

Examples

## generate data from multivariate normal with Identity precision.
pdim = 10
data = matrix(rnorm(50*pdim), ncol=pdim)

## compare different K
out1 <- PreEst.2014Banerjee(data, upperK=1)
out2 <- PreEst.2014Banerjee(data, upperK=3)
out3 <- PreEst.2014Banerjee(data, upperK=5)

## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(2,2), pty="s")
image(diag(pdim)[,pdim:1],main="Original Precision")
image(out1$C[,pdim:1], main="banded1::upperK=1")
image(out2$C[,pdim:1], main="banded1::upperK=3")
image(out3$C[,pdim:1], main="banded1::upperK=5")
par(opar)

CovTools documentation built on Aug. 14, 2021, 1:08 a.m.