A Statistical Model for Event Sequence Data

• Activity Number: 349
• Type: Contributed
• Date/Time: Tuesday, August 5, 2014 : 10:30 AM to 11:15 AM
• Sponsor: Social Statistics Section
• Author(s): Kevin Heins*+ and Hal S. Stern
• Companies: University of California, Irvine and University of California, Irvine
• Keywords: sequence models ; pattern detection ; stochastic processes ; behavior patterns ; probabilistic models

Abstract

The identification of recurring patterns within a sequence of events has become an important tool in behavior research. In this paper, we consider a general probabilistic framework for identifying such patterns, by distinguishing between events that belong to a pattern and events that occur in the background. The event processes, both for background events and events that are part of recurring patterns, are modeled as competing renewal processes. Using this framework, we develop an inference procedure to detect the sequences present in observed data. We further adapt both our model and inference procedure to accommodate group-level analyses. Our method is compared to a current approach used within the ethology literature on both simulated data and data collected to study the impact of fragmented and unpredictable maternal behavior on cognitive development of children.