  We introduce a new version of dynamic time warping for samples of observed
event times that are modeled as time-warped intensity processes. Our approach
is devel- oped within a framework where for each experimental unit or subject
in a sample, one observes a random number of event times or random locations.
As in our setting the number of observed events differs from subject to
subject, usual landmark align- ment methods that require the number of events
to be the same across subjects are not feasible. We address this challenge by
applying dynamic time warping, initially by aligning the event times for pairs
of subjects, regardless of whether the numbers of observed events within the
considered pair of subjects match. The information about pairwise alignments is
then combined to extract an overall alignment of the events for each subject
across the entire sample. This overall alignment provides a useful description
of event data and can be used as a pre-processing step for subse- quent
analysis. The method is illustrated with a historical fertility study and with
on-line auction data.
