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Activity Number: 303 - Statistical Association and High-Dimensional Data
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Nonparametric Statistics
Abstract #302867
Title: Generalized Spatially Varying Coefficient Models
Author(s): Myungjin Kim* and Li Wang
Companies: Iowa State University and Iowa State University
Keywords: Bivariate penalized splines; Generalized cross-validation; Nonparametric regression; Quasi-likelihood; Triangulation

In this paper, we introduce a new class of nonparametric regression models, called generalized spatially varying coefficient models (GSVCMs), for data distributed over complex domains. For model estimation, we propose a nonparametric quasi-likelihood approach using the bivariate penalized spline approximation technique. We show that our estimation procedure is computationally efficient, theoretically reliable, and allows irregularly shaped spatial domains with complex boundaries. We develop a numerically stable and fast algorithm using penalized iteratively reweighted least squares method to estimate the coefficient functions. Under some regularity conditions, the estimator for the coefficient function is proved to be consistent and its convergence rate is established. The finite sample performance of our models and the estimation method is examined by simulations studies. The proposed method is also illustrated by an analysis of the crash data in the Tampa–St. Petersburg–Clearwater (Metropolitan Statistical Area) MSA area in Florida.

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

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