Optimal Shrinkage Estimation using Kolmogorov-Smirnov Criterion

This function estimates the optimal shrinkage parameter for singular values using the Kolmogorov-Smirnov (KS) criterion, which helps identify noise levels in high-dimensional data.

Usage

ksOpt(singVals, betaShrinkage)

Arguments

singVals

A numeric vector of singular values from a data matrix.

betaShrinkage

A numeric value representing the aspect ratio of the data matrix (ratio of columns to rows or vice versa).

Value

A numeric value representing the estimated optimal noise level (sigma) based on the KS criterion.