BinSegBstrap-package: Piecewise smooth regression by bootstrapped binary...

Description Acknowledgement References See Also Examples

Description

Provides methods for piecewise smooth regression. The main function BinSegBstrap estimates a piecewise smooth signal by applying a bootstrapped test recursively (binary segmentation approach). A single bootstrapped test for the hypothesis that the underlying signal is smooth versus the alternative that the underlying signal contains at least one change-point can be performed by the function BstrapTest. A single change-point is estimated by the function estimateSingleCp. More details can be found in the vignette. Parts of this work were inspired by Gijbels and Goderniaux (2004).

Acknowledgement

This work results from a summer research project at the University of Cambridge in 2019. Kate McDaid was supported by a bursary from the summer research programme of the Centre of Mathematics at the University of Cambridge. Florian Pein's position is funded by the EPSRC programme grant 'StatScale: Statistical Scalability for Streaming Data'.

References

Gijbels, I., Goderniaux, A-C. (2004) Bootstrap test for change-points in nonparametric regression. Journal of Nonparametric Statistics 16(3-4), 591–611.

See Also

BinSegBstrap, BstrapTest, estimateSingleCp

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
n <- 200
signal <- sin(2 * pi * 1:n / n)
signal[51:100] <- signal[51:100] + 5
signal[151:200] <- signal[151:200] + 5

y <- rnorm(n) + signal

est <- BinSegBstrap(y = y)

plot(y)
lines(signal)
lines(est$est, col = "red")


n <- 100
signal <- sin(2 * pi * 1:n / n)
signal[51:100] <- signal[51:100] + 5

y <- rnorm(n) + signal

test <- BstrapTest(y = y)
est <- estimateSingleCp(y = y)

plot(y)
lines(signal)
lines(est$est, col = "red")

BinSegBstrap documentation built on Nov. 7, 2019, 1:07 a.m.