BinSegBstrap-package {BinSegBstrap} R Documentation

## Piecewise smooth regression by bootstrapped binary segmentation

### 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.

`BinSegBstrap`, `BstrapTest`, `estimateSingleCp`

### Examples

```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")
```

[Package BinSegBstrap version 1.0 Index]