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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Multiple Imputation for Missing Edge Data: A Predictive Evaluation Method with Application to Add Health.

Cheng Wang1, Carter T Butts2, John R Hipp3

  • 1Department of Sociology, University of Notre Dame.

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Summary
This summary is machine-generated.

Model-based imputation using Exponential Random Graph Models (ERGMs) can fill missing network data in complex surveys like Add Health. This method offers a viable approach for analyzing intricate social network studies.

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Area of Science:

  • Social network analysis
  • Statistical modeling
  • Network imputation

Background:

  • Model-based imputation for network data is theoretically possible but lacks practical application.
  • The Add Health study presents complex missing data challenges.
  • Existing methods are insufficient for intricate network imputation.

Purpose of the Study:

  • To demonstrate the practical application of model-based imputation for network data.
  • To address multiple types of missingness in the Add Health study.
  • To introduce and validate a novel cross-validation method for network imputation.

Main Methods:

  • Applied an Exponential Random Graph Model (ERGM)-based estimation and simulation approach.
  • Utilized data from 14 schools in the Add Health survey.
  • Developed the Held-Out Predictive Evaluation (HOPE) method for cross-validation.

Main Results:

  • Successfully imputed missing network data for edge variables across schools.
  • Demonstrated practical techniques for handling various missingness types.
  • HOPE validation confirmed the viability of the imputation approach.

Conclusions:

  • ERGM-based imputation is a feasible method for analyzing complex network data, such as that found in the Add Health study.
  • Careful consideration of study design is crucial for successful network imputation.
  • This approach enhances the analysis of intricate social network datasets.