Collection
Special Issue: The Hawkes Process: Theory, Methodology, Algorithms, Extension, and Applications in Environmental Sciences
Point process models are common in research as a natural tool to describe the patterns of discrete events that occur in a continuous space, time, or a space–time domain. In recent decades, the Hawkes point-process model, which was proposed by Alan G. Hawkes in the 1970s, has become one of the most useful point processes in event-type data analysis, such as earthquakes, crimes, forest fires, terrorist attacks, society networks, genomes, etc., due to its powers in detecting the clustering effect and the positive interactions among individual events/particles. Equipped with the Hawkes process and general statistical inference tools, we can determine the potential causal relationship among discrete events, especially for nowadays, with the rapid development of observation and data-storage technologies, big data has unavoidably become a hot issue in point-process data analysis. As the Hawkes process provides us with a quick tool and general framework to quantify and forecast the clustering or the triggering effect among events, it is important for us to develop more advanced theory, methodology and algorithms related to this process and its extensions, so that we can solve the challenging problems that are encountered in its applications.
In this special issue, we encourage researchers to submit their papers that are associated with the Hawkes process in, but not limited to, the following aspects:
1. Probability and statistical theories of the Hawkes process and its extended version;
2. Statistical methodologies related to the inference of the Hawkes process, such as Bayesian method, parametric and nonparametric estimation, simulation;
3. Computational algorithms related to the Hawkes process, especially machine learning and AI related;
4. Applications of the Hawkes process of data analysis in environmental, biological or agricultural sciences;
5. Review articles related to the history of the Hawkes process and/or any of the above aspects.
Editors
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Jorge Mateu
Dr. Jorge Mateu earned a bachelor's degree in 1992 from the University of Valencia (Spain) in Mathematics and Statistics, and completed his PhD in Statistics in 1998 from the same university under the supervision of Peter Diggle (Lancaster University, UK) and Francisco Montes (UV, Spain). Dr. Mateu is currently full professor of Statistics with the Department of Mathematics at University Jaume I of Castellon (Spain).
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Jiancang Zhuang
Institute of Statistical Mathematics, Japan
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Feng Chen
Dr. Feng Chen is a Senior Lecturer in Statistics at the School of Mathematics and Statistics, UNSW Sydney. His research interests include Point Processes, Recurrent Event Data Analysis, Nonparametric Methods, Asymptotic Theory, Statistical Computing, and Applied Statistics.
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Rick Schoenberg
Dr. Rick Schoenberg is Professor of Statistics at UCLA, and was chair of the department from 2012-2015 and vice chair from 2006-2012. His research focuses on spatial-temporal point processes, and their applications especially to the study of earthquakes, epidemics, wildfires, and crimes. He is also the founder and editor of the Journal of Environmental Statistics and Associate Editor of the Annals of Applied Statistics.
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Jing (Maggie) Chen
Cardiff University, UK
