CV_VARMLE: cross-validation for transition matrix update in maximization...
In hdiVAR: Statistical Inference for Noisy Vector Autoregression

CV_VARMLE

R Documentation

cross-validation for transition matrix update in maximization step

Tune the tolerance parameter of generalized Dantzig selector and hard thresholding level via prediction error in test data.

CV_VARMLE(tol_seq, ht_seq, S0_train, S1_train, Y_test, is_echo = FALSE)

`tol_seq`	vector; grid of tolerance parameter in Dantzig selector for cross-validation.
`ht_seq`	vector; grid of hard-thresholding levels for transition matrix estimate. To avoid hard thresholding, set `ht_seq=0`.
`S0_train`	a p by p matrix; average (over time points in training data) of conditional expectation of `x_t x_t^\top` on `y_1, \ldots, y_T` and parameter estimates, obtained from expectation step.
`S1_train`	a p by p matrix; average (over time points in training data) of conditional expectation of `x_t x_{t+1}^\top`on `y_1, \ldots, y_T` and parameter estimates, obtained from expectation step.
`Y_test`	a p by T_test matrix; observations of time series in test set.
`is_echo`	logical; if true, display the information of CV-optimal (tol, ht).

a list of CV-optimal parameters and test prediction error.

`tol_min`	CV-optimal tolerance parameter in Dantzig selector.
`ht_min`	CV-optimal hard thresholding level for the output of Dantzig selector.
`test_loss`	a matrix of prediction error in test data; columns match `tol_seq`, and rows match `ht_seq`.