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Activity Number:
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507
- Business, Time Series, and Spatial Analysis Methods
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Type:
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Contributed
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Date/Time:
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Thursday, August 6, 2020 : 10:00 AM to 2:00 PM
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Sponsor:
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Section on Statistical Computing
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Abstract #313764
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Title:
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Spatial Automatic Subgroup Analysis for Areal Data with Repeated Measures
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Author(s):
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Xin Wang* and Zhengyuan Zhu and Hao Helen Zhang
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Companies:
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Miami University and Iowa State University and University of Arizona
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Keywords:
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Areal data;
Linear regression;
Penalization;
Repeated measures;
Spatial clustering;
Subgroup analysis
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Abstract:
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We consider the subgroup analysis problem for spatial areal data with repeated measures. We propose to use a class of spatially-weighted concave pairwise fusion method which minimizes the objective function subject to a weighted pairwise penalty, referred as Spatial automatic Subgroup analysis (SaSa) . The penalty is imposed on all pairs of observations, with the location specific weight being chosen for each pair based on their corresponding spatial information. The alternating direction method of multiplier algorithm (ADMM) is applied to obtain the estimates. We show that the oracle estimator based on weighted least squares is a local minimizer of the objective function with probability approaching 1 under some conditions, which also indicates the clustering consistency properties. In simulation studies, we demonstrate the performances of the proposed method equipped with different weights in terms of their accuracy for estimating the number of subgroups. The results suggest that spatial information can enhance subgroup analysis in certain challenging situations when the minimal group difference is small or the number of repeated measures is small.
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Authors who are presenting talks have a * after their name.
