A composite likelihood approach for spatially correlated survival data

The aim of this paper is to provide a composite likelihood approach to handle spatially correlated survival data using pairwise joint distributions. With e-commerce data, a recent question of interest in marketing research has been to describe spatially clustered purchasing behavior and to assess whether geographic distance is the appropriate metric to describe purchasing dependence. We present a model for the dependence structure of time-to-event data subject to spatial dependence to characterize purchasing behavior from the motivating example from e-commerce data. We assume the Farlie–Gumbel–Morgenstern (FGM) distribution and then model the dependence parameter as a function of geographic and demographic pairwise distances. For estimation of the dependence parameters, we present pairwise composite likelihood equations. We prove that the resulting estimators exhibit key properties of consistency and asymptotic normality under certain regularity conditions in the increasing-domain framework of spatial asymptotic theory.

► We present a model to handle survival data subject to spatial dependence. ► We use pairwise joint distributions and model the dependence parameter to characterize the dependence structure. ► We present pairwise composite likelihood equations to estimate the dependence parameters. ► We apply the model to a motivating example using e-commerce data. ► The resulting estimators are consistent and asymptotically normal under certain regularity conditions.