sic.penalty | R Documentation |

The function evaluates the penalty term for Schwarz Information Criterion.
If `alpha`

is greater than 1, the strengthen SIC proposed proposed in Fryzlewicz (2014) is calculated. This routine is typically not called directly by the user;
its name can be passed as an argument to `features`

.

sic.penalty(n, n.param, alpha = 1, ...)

`n` |
The number of observations. |

`n.param` |
The number of parameters in the model for which the penalty is evaluated. |

`alpha` |
A scalar greater or equal than one. |

`...` |
Not in use. |

the penalty term *n.param * (log(n))^(alpha)*.

R. Baranowski, Y. Chen, and P. Fryzlewicz (2019). Narrowest-Over-Threshold Change-Point Detection. (http://stats.lse.ac.uk/fryzlewicz/not/not.pdf)

P. Fryzlewicz (2014). Wild Binary Segmentation for multiple change-point detection. Annals of Statistics. (http://stats.lse.ac.uk/fryzlewicz/wbs/wbs.pdf)

#*** a simple example how to use the AIC penalty x <- rnorm(300) + c(rep(1,50),rep(0,250)) w <- not(x) w.cpt <- features(w, penalty="sic") w.cpt$cpt[[1]]

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