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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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How do multiple testing correction and spatial autocorrelation affect areal boundary analysis?

Pierre Goovaerts1

  • 1BioMedware, Inc., 3526 W Liberty, Suite 100, Ann Arbor, MI 48103, USA. goovaerts@biomedware.com

Spatial and Spatio-Temporal Epidemiology
|December 15, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces improved boundary analysis methods to detect rate changes, enhancing accuracy by considering spatial patterns and population sizes. These advancements improve edge detection and reduce false positives in spatial data analysis.

Keywords:
Cervix cancerfalse discovery ratelung cancerneutral modelsemivariogramstochastic simulation

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Area of Science:

  • Spatial analysis
  • Statistical modeling
  • Geographic information systems

Background:

  • Boundary analysis is crucial for identifying rate changes in spatial data.
  • Existing methods may not adequately account for spatial autocorrelation and population heterogeneity.
  • Accurate detection of boundaries is essential for various applications, including epidemiology and environmental science.

Purpose of the Study:

  • To present novel implementations for boundary analysis.
  • To quantify rate changes using absolute and relative measures.
  • To improve the detection of significant changes and address multiple testing issues.

Main Methods:

  • Developed alternative boundary analysis techniques.
  • Incorporated spatial patterns and population sizes into randomization procedures.
  • Utilized a null hypothesis of non-uniform risk for change detection.
  • Computed multi-edge p-values for multiple testing correction.

Main Results:

  • Accounting for spatial autocorrelation and population size enhanced discriminant power and edge detection accuracy (van Rijsbergen’s F-measure).
  • Tests based on non-uniform risk showed the most significant improvements in power and F-measure.
  • Multiple testing correction reduced false positives while slightly impacting classification accuracy.

Conclusions:

  • The proposed boundary analysis methods offer improved accuracy and robustness.
  • Considering spatial factors and employing non-uniform risk hypotheses are key to effective boundary detection.
  • The multi-edge p-value approach effectively controls false positives in spatial analyses.