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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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Sparse spatially clustered coefficient model via adaptive regularization.

Yan Zhong1, Huiyan Sang2, Scott J Cook3

  • 1KLATASDS-MOE, School of Statistics, East China Normal University, China.

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|August 3, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new regression method to identify key factors influencing spatial data and reveal how these factors change across regions. The approach effectively analyzes complex spatial patterns in covariate effects, such as those seen in COVID-19 vaccine acceptance.

Keywords:
COVID-19 vaccination acceptanceSpatial variable selectionVariable-dependent graphVarying coefficient regression

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Area of Science:

  • Spatial statistics
  • Statistical modeling
  • Geospatial analysis

Background:

  • Large spatial datasets are increasingly common across various scientific fields.
  • Identifying influential covariates and their spatially varying effects is a critical challenge.
  • Understanding regional variations and abrupt changes in covariate effects is essential.

Purpose of the Study:

  • To propose an efficient regularized spatially clustered coefficient (RSCC) regression approach.
  • To simultaneously perform variable selection and identify spatially heterogeneous covariate effects with clustered patterns.
  • To develop a computationally efficient method for analyzing large spatial datasets.

Main Methods:

  • Developed a novel RSCC regression approach.
  • Incorporated a chain graph guided fusion penalty and a group lasso penalty for regularization.
  • Utilized adaptive learning for weights and graphs to enhance estimation performance.

Main Results:

  • The RSCC model efficiently handles large spatial datasets while ensuring estimation guarantees.
  • Successfully identified spatially clustered patterns in covariate effects.
  • Applied to US county-level COVID-19 vaccine acceptance data, revealing significant spatial variations.

Conclusions:

  • RSCC is an effective tool for variable selection and uncovering complex spatial heterogeneity in covariate effects.
  • The method reveals important within-state and across-state spatially clustered patterns.
  • Provides valuable insights into factors influencing spatial phenomena like vaccine acceptance.