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Multivariate point process models in social research

Affiliation:	1. Department of Scientific Computing, Florida State University, Tallahassee, FL, United States;2. Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, Australia;3. School of Business, Stevens Institute of Technology, Hoboken, NJ, United States;1. Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Kampus UI Depok, 16424, Depok, Indonesia;2. Faculty of Computer Science, Universitas Indonesia, Kampus UI Depok, 16424, Depok, Indonesia;1. Department of Artificial Intelligence, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu, South Korea;2. Marine Disaster Research Center, Korea Institute of Ocean Science and Technology, 385, Haeyang-ro, Yeongdo-gu, Busan, South Korea;3. School of Computer Science and Engineering, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu, South Korea

Abstract:	This paper advocates the use of multivariate point processes for modeling dynamic processes where several types of discrete outcomes occur repeatedly over time. The first section provides an overview of point process models with emphasis on the intensity function specification. The second section discusses complete and incomplete likelihood techniques for estimating parameters and briefly reviews the advantages and disadvantages of the two techniques. The paper ends with an empirical example from organizational sociology that illustrates the application of a multivariate point process model and complete likelihood estimation.

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