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structSSI: Simultaneous and Selective Inference for Grouped or Hierarchically Structured Data.

Kris Sankaran1, Susan Holmes2

  • 1Department of Statistics, Stanford University, P.O. Box 14869, Stanford CA, 94309, United States of America, krissankaran@stanford.edu URL: http://www.stanford.edu/~kriss1.

Journal of Statistical Software
|February 27, 2016
PubMed
Summary
This summary is machine-generated.

The structSSI R package offers new methods for simultaneous and selective inference, enhancing statistical power and interpretability by accounting for hypothesis dependencies. These tools improve analysis for complex data structures.

Keywords:
false discovery ratehierarchical datamultiple testingselective inferencesimultaneous inference

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Area of Science:

  • Statistics
  • Bioinformatics
  • Ecological Modeling

Background:

  • Traditional multiple testing methods often overlook complex dependencies between hypotheses.
  • Controlling the false discovery rate (FDR) is crucial in high-dimensional data analysis.
  • Existing techniques may lack the power to detect true effects when hypotheses are structured.

Purpose of the Study:

  • To introduce the structSSI R package for implementing group Benjamini-Hochberg and hierarchical FDR procedures.
  • To provide accessible tools for simultaneous and selective inference that leverage hypothesis dependence.
  • To enhance statistical power and interpretability in hypothesis testing by incorporating structural information.

Main Methods:

  • Implementation of group Benjamini-Hochberg and hierarchical false discovery rate procedures in R.
  • Development of functions within the structSSI package to establish dependence structures.
  • Application of these procedures to ecological microbial abundance data and global temperature time series.

Main Results:

  • Demonstrated increased statistical power and improved interpretability compared to standard methods.
  • Successfully applied novel inference techniques to real-world ecological and climate data.
  • Provided a framework for encoding complex dependencies between hypotheses in statistical analyses.

Conclusions:

  • The structSSI package offers a powerful and accessible solution for simultaneous and selective inference.
  • Incorporating hypothesis structure significantly enhances the performance of FDR control methods.
  • These procedures are broadly applicable to diverse datasets with inherent dependencies.