sic.penalty: Schwarz Information Criterion penalty

View source: R/penalties.R

sic.penaltyR Documentation

Schwarz Information Criterion penalty

Description

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.

Usage

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

Arguments

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.

Value

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

References

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)

Examples

#*** 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]]

not documentation built on March 18, 2022, 7:24 p.m.