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Activity Number:
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189
- Nonparametric Methods in Big or Complex Data
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Type:
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Contributed
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Date/Time:
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Tuesday, August 4, 2020 : 10:00 AM to 2:00 PM
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Sponsor:
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Section on Nonparametric Statistics
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Abstract #313603
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Title:
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Spatiotemporal Autoregressive Partially Linear Varying Coefficient Models
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Author(s):
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Shan Yu* and Lily Wang and Lei Gao
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Companies:
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Iowa State University and Iowa State University and Iowa State University
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Keywords:
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Spatiotemporal data analysis;
Nonstationarity;
Semiparametric regression;
Varying coefficient models;
Partially linear models;
Tensor product spline
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Abstract:
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The wide availability of economic data observed over time and space has stimulated many studies in the past two decades. This project targets on developing a class of spatiotemporal autoregressive partially linear varying coefficient models that are sufficiently flexible to simultaneously capture the spatiotemporal dependence and nonstationarity often encountered in practice. When spatial observations are observed over time and exhibit dynamic and nonstationary behaviors, our models become a useful tool for analyzing such data. We develop a numerically stable and computationally fast estimation procedure using the tensor product splines over triangular prisms to approximate the coefficient functions. The estimators of both the constant coefficients and varying coefficients are consistent. We also show that the estimators of the constant coefficients are asymptotically normal, which enables us to construct confidence intervals and make inferences. The performance of the method is evaluated by Monte Carlo experiments and applied to the Sydney housing dataset.
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Authors who are presenting talks have a * after their name.
