A0810
Title: Generalized point process additive models
Authors: Kuang-Yao Lee - Temple University (United States) [presenting]
Abstract: A generalized point process additive model with a scalar response and high-dimensional point process predictors is proposed. The proposal is built upon four key components: a realization of a point process as a random counting measure, a generalized point process regression framework, a new kernel function for random measure through kernel embedding, and a suite of low-dimensional structures, including the additive model, reduced basis representation, and sparsity. An eclient penalized likelihood procedure is developed for model estimation, and both the estimation consistency and selection consistency of the estimator are established while allowing the number of point process predictors to diverge. The method is illustrated and evaluated through simulations and an electronic health record data application.
