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Sample-wise Combined Missing Effect Model with Penalization.

Jialu Li1, Guan Yu2, Qizhai Li3

  • 1School of Mathematics and Statistics, Beijing Institute of Technology.

Journal of Computational and Graphical Statistics : a Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
|June 5, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces Sample-wise Combined missing effect Model with penalization (SCOM), a novel method for handling missing data in high-dimensional statistics. SCOM effectively utilizes all data and avoids imputation errors, offering a robust solution for statistical inference.

Keywords:
ImputationLassoLinear regressionMissing dataRidge regression

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Area of Science:

  • Statistics
  • Statistical Inference
  • Data Science

Background:

  • Missing data is a common challenge in high-dimensional statistical inference.
  • Existing methods like complete-sample analysis and imputation have limitations, including information loss and accumulated errors.
  • There is a need for robust methods that fully utilize available data.

Purpose of the Study:

  • To propose a new method, Sample-wise Combined missing effect Model with penalization (SCOM), for addressing missing data in predictors.
  • To develop a method that avoids predictor imputation and estimates the combined effect of missing data per sample.
  • To ensure the method is robust across various missing data mechanisms.

Main Methods:

  • Developed the Sample-wise Combined missing effect Model with penalization (SCOM).
  • SCOM estimates the combined missing effect for each incomplete sample, rather than imputing predictors.
  • Theoretical analysis includes oracle inequality and consistency of variable and missing effect selection.

Main Results:

  • SCOM makes full use of all available data.
  • The method demonstrates robustness with respect to various missing mechanisms.
  • Theoretical guarantees include oracle inequality and consistency in selection.
  • Simulation studies and a real-data application confirm SCOM's effectiveness.

Conclusions:

  • SCOM offers an effective and robust approach to handling missing data in high-dimensional statistical inference.
  • The method's ability to utilize all data and avoid imputation errors provides significant advantages.
  • SCOM shows promise for improving statistical modeling in the presence of missing predictor data.