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I am using the bcp package in R to determine change points in a time series. The output that this package gives is a distribution of posterior probabilities.

As far as I can understand, the peak points in this distribution must be change points, but this way I am getting a lot of false positives as in the original data, not all of the peak points are coming out to be change points.

There must be a threshold value that I can consider to declare a peak as a change point. How can I determine that? Is there any other way to find the change points?

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This is essentially the difference between the output from a frequentist and Bayesian procedure. To get frequentist type output i.e. point estimates one typically uses the Bayesian Maximum a posteriori (MAP) estimation. This essentially chooses a threshold and returns the modes above that threshold. You are then back to the frequentist world of "what threshold is appropriate?" which is the same as asking "how long is a piece of string?".

Typically people use thresholds based on the Bayes Factor, for example:

http://www.lancs.ac.uk/~killick/Pub/EckleyFearnheadKillick2010.pdf

shows how this choice can be made and:

http://eprints.lancs.ac.uk/8189/1/PScpt2.pdf

provides a histogram of number of changes against posterior probability too indicating that the mode of that histogram should be chosen.

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