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An expectation maximization algorithm for high-dimensional model selection for the Ising model with misclassified

David G Sinclair1, Giles Hooker1

  • 1Department of Statistical Science, Cornell University, Ithaca, NY, USA.

Journal of Applied Statistics
|November 10, 2022
PubMed
Summary
This summary is machine-generated.

We introduce the misclassified Ising Model for analyzing binary data with errors. Our method accurately identifies relationships in graphical models even with data misclassification.

Keywords:
Graphical modelsLASSOfMRIlatent variablesvariational methods

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Area of Science:

  • Statistical modeling
  • Machine learning
  • Network analysis

Background:

  • Dependent binary data analysis is crucial in many fields.
  • Existing graphical models struggle with binary data susceptible to errors.
  • Accurate model selection is vital for understanding complex systems.

Purpose of the Study:

  • To propose the misclassified Ising Model for dependent binary data with errors.
  • To extend existing model selection methods to handle misclassification.
  • To develop an expectation maximization algorithm for improved model selection.

Main Methods:

  • Applying the LASSO to logistic regression at each node.
  • Extending theoretical results for graphical model selection.
  • Developing an expectation maximization algorithm to account for misclassification.

Main Results:

  • The proposed method correctly identifies edges in the underlying graphical model under misclassification.
  • The expectation maximization algorithm demonstrates improved performance with simulated data.
  • The framework is validated using functional magnetic resonance imaging data.

Conclusions:

  • The misclassified Ising Model provides a robust framework for analyzing error-prone binary data.
  • The expectation maximization algorithm enhances model selection accuracy in the presence of misclassification.
  • This approach has potential applications in neuroimaging and other data-intensive fields.