Description Usage Arguments Value Examples

This function implements the Dynamic Connectivity Regression (DCR) algorithm proposed by Cribben el al. (2012) to locate changepoints.

1 2 3 4 5 6 7 8 9 10 11 | ```
detectGlasso(
Y,
Del,
p,
lambda = "bic",
nboot = 100,
n.cl,
bound = c(0.001, 1),
gridTF = FALSE,
plotTF = TRUE
)
``` |

`Y` |
Input data of dimension length*dim (T times d) |

`Del` |
Delta away from the boundary restriction |

`p` |
Gep(p) distribution controls the size of stationary bootstrap. The mean block length is 1/p |

`lambda` |
two selections possible for optimal parameter of lambda. "bic" finds lambda from bic criteria, or user can directly input the penalty value |

`nboot` |
the number of bootstrap sample for pvalue. Default is 100. |

`n.cl` |
number of cores in parallel computing. The default is (machine cores - 1) |

`bound` |
bound of bic search in "bic" rule. Default is (.001, 1) |

`gridTF` |
minimum bic is found by grid search. Default is FALSE |

`plotTF` |
Draw plot to see test statistic |

A list with component

**br** The estimated breakpoints including boundary (0, T)

**brhist** The sequence of breakspoints found from binay splitting

**diffhist** The history of BIC reduction on each step

**W** The estimated vecorized autocovariance on each regime.

**WI** The estimated vecorized precision matrix on each regime.

**lambda** The penalty parameter estimated on each regime.

**pvalhist** The empirical p-values on each binary spltting.

**fitzero** Detailed output at first stage. Useful in producing plot.

1 | ```
out1= detectGlasso(changesim, p=.2, n.cl=1)
``` |

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