Restricted Spatial Regression: Methods Implications

by Khan, Kori

Type Thesis
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In our increasingly data rich world, large data sets containing locational information (e.g., latitude and longitude coordinates) are becoming abundant. Researchers in myriad fields such as epidemiology, geology, and ecology often use such datasets. To analyze spatially-referenced data effectively, however, researchers typically cannot use traditional regression methods. This is because such methods do not account for spatial dependence in the data (i.e., the fact that nearby observations are m…

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Summary

In our increasingly data rich world, large data sets containing locational information (e.g., latitude and longitude coordinates) are becoming abundant. Researchers in myriad fields such as epidemiology, geology, and ecology often use such datasets. To analyze spatially-referenced data effectively, however, researchers typically cannot use traditional regression methods. This is because such methods do not account for spatial dependence in the data (i.e., the fact that nearby observations are more alike than distant observations). Instead, spatial regression models are commonly employed.

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Edition Statements

  • Publication Date:2020

Thesis Information

  • Degree:Ph.D.
  • University:The Ohio State University
  • Advisor/Supervisor:Calder, Catherine;Hans, Christopher
  • Pages:165

Publication Statements

  • Language:eng
  • Publication Date:2020

Edition Identifiers

  • proquest:2572606873

Subjects

Dataset eng Information Science PDF Regression coefficient Restricted spatial regression Statistical physics Statistics

Writers

Khan, Kori Calder, Catherine;Hans, Christopher

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