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On the use of comparison regions in visualizing stochastic uncertainty in some two-parameter estimation problems
1Institute of Medical Biometry and Statistics, Section of Health Care Research and Rehabilitation Research, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.
Biometrical Journal. Biometrische Zeitschrift
|October 30, 2019
Summary
We introduce comparison regions, a novel visualization tool for statistical inference. This method aids in demonstrating evidence for two parameters within specific convex subsets of the parameter space, enhancing post hoc analyses.
Area of Science:
- Statistics
- Statistical Inference
- Data Visualization
Background:
- Two-dimensional confidence regions are standard for visualizing simultaneous inference and testing single-point hypotheses.
- Current methods are limited when the interest lies in demonstrating parameter values within specific convex subsets.
Purpose of the Study:
- To introduce a new visualization tool, comparison regions, for post hoc statistical inference.
- To enable the demonstration of evidence for parameters falling within user-defined convex subsets.
Main Methods:
- Development of comparison regions as a visualization technique.
- Application in post hoc analysis for simultaneous inference on two parameters.
Main Results:
- Comparison regions provide a straightforward method for analyzing hypotheses concerning convex subsets.
- Facilitates the visualization of stochastic uncertainty for two parameters within defined regions.
Conclusions:
- Comparison regions offer a flexible and effective tool for advanced statistical inference.
- Enhances the capability to test complex hypotheses in post hoc analyses involving two parameters.
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