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Title: Statistical methods for replicated spatio-temporal point processes Authors:  Daniel Gervini - University of Wisconsin-Milwaukee (United States) [presenting]
Abstract: Spatio-temporal point processes are widely used in statistics. However, the literature has mostly focused on the single-realization scenario. When many replications of a temporal point process are available at various spatial points, inferential tools such as kriging can be simplified and questionable assumptions such as isotropy are not necessary. We will introduce these new methods, based on doubly-stochastic Poisson process models, and show their application in the analysis of spatial and temporal bike demand in the Divvy shared-bicycle system of the city of Chicago.