cv.tuning.selection: a function to apply cross-validation to select tuning...

In VARDetect: Multiple Change Point Detection in Structural VAR Models

a function to apply cross-validation to select tuning parameter by minimizing SSE

Description

a function to apply cross-validation to select tuning parameter by minimizing SSE

Usage

cv.tuning.selection(data, lambda.seq, mu.seq, alpha_L = 0.25, nfold = 5)

Arguments

data	a n by p dataset matrix
lambda.seq	a numeric vector, indicates the sequence of tuning parameters of sparse components
mu.seq	a numeric vector, the sequence of tuning parameters of low rank components
alpha_L	a positive numeric value, indicating the constraint space of low rank components
nfold	a positive integer, the number of folds for cv

Value

a list of object, including

grid

the grid of lamdbas and mus

lambda

final selected tuning parameter for sparse

mu

final selected tuning parameter for low rank

VARDetect documentation built on May 10, 2022, 9:07 a.m.