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Activity Number: 21 - Spatial and Spatio-Temporal Statistics for Biomedical and Epidemiological Studies
Type: Topic Contributed
Date/Time: Sunday, July 29, 2018 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #328321 Presentation
Title: Spatially Varying Coefficient Models for Point Pattern Analysis with Large Data Sets
Author(s): Huiyan Sang*
Companies: Texas A&M University
Keywords: Epidemiology; Point pattern; Spatial Statistics; Varying coefficient

Numerous problems in geosciences, epidemiology, traffic planning and crime research nowadays involve large amounts of spatial point pattern data recording event occurrence. In many such applications, a main problem of interest is to characterize the probability of event occurrence and its relationship with a set of covariates, considering spatial dependence of observations. Spatial Poisson point process models are commonly used for the analysis of point patterns, in which the intensity function is assumed to be a function of a number of covariates. In this study, we seek to develop a spatial varying coefficient model for point patterns with large data via regularization. We propose a computationally efficient algorithm for parameter estimations and establish theoretical properties of the proposed estimators. We illustrate the performance of our model via both simulation studies and real data examples.

Authors who are presenting talks have a * after their name.

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