in a variety of fields work with sequential data, such as measurements made
over time. In some instances, one or
more of the moments (e.g., mean, variance) of the series may change abruptly at
some point in the sequence, yielding what is known as a change point. There is a broad literature describing
methods for change point detection.
Researchers working with multiple sequential series containing change
points may be interested in comparing the locations of these changes. Recently, a method for comparing the
locations of 2 or more change points in 2 or more series using a Bayesian
estimator has been described in the literature.
The purpose of the current Monte Carlo simulation study was to extend
this earlier work by assessing the performance of this approach with time
series of between 20 and 200 measurements in length, for a normally distributed
measurement process. Results of the
simulation revealed that the method always controlled the Type I error rate,
and had power of 0.75 or higher for series of 50 measurements or longer, when
the variance in measurements was relatively low.
Keywords: Change point, Bayesian
analysis, Time series
Classification: C11, C14, C22