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Activity Number:
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171
- New Nonparametric Methods for Correlated Data
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2018 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract #330809
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Presentation
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Title:
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Spatially Varying Coefficient Autoregressive Models
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Author(s):
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Jingru Mu* and Guannan Wang and Lily Wang
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Companies:
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Iowa State University and College of William & Mary and Iowa State University
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Keywords:
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spatial interaction;
bivariate splines;
smoothing;
penalty;
spatially varying coefficient models;
triangulation
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Abstract:
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In this paper, we study the estimation of spatially varying coefficient autoregressive models for data distributed over complex domains. We weigh the influence of the neighborhoods by calculating geo-distance and use bivariate splines over triangulations to represent the coefficient functions. The estimators of the coefficient functions are consistent, and rates of convergence of the proposed estimators are established. We propose hypothesis tests to examine the dependence among neighborhoods and test whether the coefficient function is really varying over space. The proposed method is much more computationally efficient than the well-known geographically weighted regression technique and thus usable for analyzing massive datasets. The performance of the estimators and the proposed tests are evaluated by a few simulation examples and a real data analysis.
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Authors who are presenting talks have a * after their name.
