README.md

In trambakbanerjee/casp: Coordinate-wise Adaptive Shrinkage Prediction

casp

This is an R package for Coordinate-wise Adaptive Shrinkage Prediction (casp) in a high-dimensional non-exchangeable hierarchical Gaussian model with unknown location as well as an unknown spiked covariance structure. CASP [1] uses results on the behavior of eigenvalues and eigenvectors of high-dimensional sample covariance matrix ([2], [3], [4]) to develop a bias-correction principle that leads to an efficient approach for evaluating the Bayes predictors corresponding to popular loss functions such as quadratic, generalized absolute, and Linex losses.

Installation

You can install casp using devtools.

R devtools::install_github("trambakbanerjee/casp")

If you are looking for the R scripts that reproduce the analysis in the paper [1] then please use the folder Numerical Experiments for more information.

References

[1.] Improved Shrinkage Prediction under a Spiked Covariance Structure Banerjee, T., Mukherjee, G. and Paul, D. Journal of Machine Learning Research, 22 (2021): 180-1.

[2.] Baik, J. and J. W. Silverstein (2006). Eigenvalues of large sample covariance matrices of spiked population models. Journal of Multivariate Analysis 97(6), 1382–1408.

[3.] Onatski, A. (2012). Asymptotics of the principal components estimator of large factor models with weakly influential factors. Journal of Econometrics 168(2), 244–258.

[4.] Paul, D. (2007). Asymptotics of sample eigenstructure for a large dimensional spiked covariance model. Statistica Sinica, 1617–1642.

trambakbanerjee/casp documentation built on Nov. 22, 2022, 7:24 p.m.