Description Usage Arguments Details Value References Examples

Apply K-fold cross-validation for selecting tuning parameters for banding covariance matrix using grid search strategy

1 | ```
banding.cv(matrix, n.cv = 10, norm = "F", seed = 142857)
``` |

`matrix` |
a N*p matrix, N indicates sample size and p indicates the dimension |

`n.cv` |
times that cross-validation repeated, the default number is 10 |

`norm` |
the norms used to measure the cross-validation errors, which can be the Frobenius norm "F" or the operator norm "O" |

`seed` |
random seed, the default value is 142857 |

For cross-validation, this function split the sample randomly into two pieces of size n1 = n-n/log(n) and n2 = n/log(n), and repeat this k times

An object of class "CovCv" containing the cross-validation's result for covariance matrix regularization, including:

`regularization` |
regularization method, which is "Banding" |

`parameter.opt` |
selected optimal parameter by cross-validation |

`cv.error` |
the corresponding cross-validation errors |

`n.cv` |
times that cross-validation repeated |

`norm` |
the norm used to measure the cross-validation error |

`seed` |
random seed |

"High-Dimensional Covariance Estimation" by Mohsen Pourahmadi

1 2 3 4 5 6 | ```
data(m.excess.c10sp9003)
retcov.cv <- banding.cv(m.excess.c10sp9003, n.cv = 10,
norm = "F", seed = 142857)
summary(retcov.cv)
plot(retcov.cv)
# Low dimension
``` |

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