B1148
Title: Multivariate temporal point process regression
Authors: Lexin Li - University of California Berkeley (United States)
Xiwei Tang - University of Virginia (United States) [presenting]
Abstract: Point process modeling is gaining increasing attention, as point process type data are emerging in a large variety of scientific applications. Motivated by a neuronal spike trains study, we propose a novel point process regression model, where both the response and the predictor can be a high-dimensional point process. We model the predictor effects through the conditional intensities using a set of basis transferring functions in a convolutional fashion. We organize the corresponding transferring coefficients in the form of a three-way tensor, then impose the low-rank, sparsity, and subgroup structures on this coefficient tensor. These structures help reduce dimensionality, integrate information across different individual processes, and facilitate interpretation. We develop a highly scalable optimization algorithm for parameter estimation. We derive the large sample error bound for the recovered coefficient tensor, and establish the subgroup identification consistency, while allowing the dimension of the multivariate point process to diverge. We demonstrate the efficacy of our method through both simulations and a cross-area neuronal spike trains analysis in a sensory cortex study.
