|Multivariate data is segmented into parts, called segments, with
common characteristics. The segments are assumed to have an under-
lying model structure. It is of interest to see whether the character-
istic changes originate from a subset of features or a unique feature.
Various approaches can be taken when constructing such a method
for multiple features. Three di erent methods are devised and com-
pared via a simulation study. The methods use a penalized likelihood
in di erent ways to estimate the number of segments. Two of these
methods exhibit positive results and are examined further. They are
shown to have di erent capabilities. One method favors the detection
of coordinated segment changes at the expense of nding those that
originate in a unique feature. The other method has an overall better
performance, i.e., it is better at locating each and every characteristic
change. The two methods are applied in two real life settings, one
measuring physical changes in coordination with various music and
the other measuring a range of physical changes in epilepsy patients.
The methods capture the trends in the data but are not able to detect
precisely when the music changes or the beginning and the end of a
|Joint segmentation of multiple features with application to analysis of epilepsy and music.
|University of Gothenburg/Department of Mathematical Science
|Göteborgs universitet/Institutionen för matematiska vetenskaper