A0266
Title: A generalized functional linear model with spatial dependence
Authors: Sooran Kim - Iowa State University (United States) [presenting]
Xiongtao Dai - University of California Berkeley (United States)
Mark Kaiser - Iowa State University (United States)
Abstract: A regression model is developed for spatially dependent binary response variables when the covariates form functional processes over time at each location for which the response is observed. The functional covariates are modelled in terms of a Fourier basis truncated to a finite number of terms. Responses are considered Markov random fields with conditional binary distributions and isotropic spatial dependence. Estimation is approached using a composite likelihood constructed from full conditional response distributions, sometimes also called Besags original pseudolikelihood in the spatial literature. Asymptotic properties are given for maximum composite likelihood estimators using a repeating lattice context, and the use of the model is illustrated with data relating new COVID vaccination rates in June for counties to the number of weekly infections reported over the previous several months in those same counties.
