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Heins K, Stern H. A Statistical Model for Event Sequence Data. JMLR Workshop Conf Proc. 2014;33:338-346
Heins, K., & Stern, H. (2014). A Statistical Model for Event Sequence Data. JMLR workshop and conference proceedings, 33338-346.
Heins, Kevin, and Stern, Hal. "A Statistical Model for Event Sequence Data." JMLR workshop and conference proceedings vol. 33 (2014): 338-346.
Heins K, Stern H. A Statistical Model for Event Sequence Data. JMLR Workshop Conf Proc. 2014;33:338-346. PMID: 28435511; PMCID: PMC5397901.
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JMLR Workshop Conf Proc. 2014;33:338-346.

A Statistical Model for Event Sequence Data.

JMLR workshop and conference proceedings

Kevin Heins, Hal Stern

Affiliations

  1. Department of Statistics, University of California, Irvine.

PMID: 28435511 PMCID: PMC5397901

Abstract

The identification of recurring patterns within a sequence of events is an important task 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 as part of background processes. 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. 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.

References

  1. Behav Res Methods Instrum Comput. 2000 Feb;32(1):93-110 - PubMed
  2. J Comput Biol. 2004;11(2-3):319-55 - PubMed
  3. Am J Psychiatry. 2012 Sep;169(9):907-15 - PubMed

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