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Area of Science:

  • Spatial statistics
  • Geostatistics
  • Statistical modeling

Background:

  • The range of influence quantifies spatial correlation decay.
  • Missing data is a common challenge in spatial studies.
  • Multiple imputation is a technique for handling missing data.

Purpose of the Study:

  • To assess the impact of partially observed outcomes on range of influence estimation.
  • To evaluate the effectiveness of multiple imputation in addressing missing spatial data.
  • To understand how missing data mechanisms influence spatial correlation estimates.

Main Methods:

  • Simulated missing outcomes within a complete dataset.
  • Estimated range of influence using logistic regression with Gaussian fields.
  • Compared range estimates from complete, incomplete, and multiply imputed data.

Main Results:

  • Range estimates were generally consistent with up to 25% missing data.
  • Moderate missingness sometimes impacted range estimates.
  • Multiple imputation showed potential for improvement with >=50% missing data, but increased uncertainty.

Conclusions:

  • Missing data effects on range of influence vary with the missing data mechanism.
  • The overall impact of missing observations was minor relative to inherent estimate uncertainty.