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Updated: Jun 9, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Bayesian partially-protected regularization as a model selection tool.

Yasir Atalan1, Selim Yaman2, Jeff Gill3

  • 1Department of Government, American University, Washington, DC, USA.

Journal of Applied Statistics
|June 8, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces Bayesian Partially-Protected Lasso (BPL) and Bayesian Protected Elastic Net (BPEN) to machine learning. These methods allow researchers to protect theoretically important variables while exploring large datasets efficiently.

Keywords:
Bayesian statisticsLassoelastic netmachine learningpredictive modelingregularization

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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Establishing a Competing Risk Regression Nomogram Model for Survival Data

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Last Updated: Jun 9, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Area of Science:

  • Statistical modeling
  • Machine learning
  • Bioinformatics

Background:

  • Traditional Lasso methods can shrink important variables to zero.
  • Researchers need methods to balance variable selection with theoretical importance.

Purpose of the Study:

  • Introduce Bayesian Partially-Protected Lasso (BPL) and Bayesian Protected Elastic Net (BPEN).
  • Enable efficient exploration of large datasets while protecting key predictors.
  • Combine Lasso/Elastic Net flexibility with theoretical variable safeguarding.

Main Methods:

  • Developed Bayesian Partially-Protected Lasso (BPL).
  • Introduced Bayesian Protected Elastic Net (BPEN) building on BPL.
  • Provided statistical background, algorithms, and an R package for tools.

Main Results:

  • BPL allows identification of protected and non-protected variables.
  • BPEN combines Elastic Net's robustness with protected variable integrity.
  • Facilitates machine exploration of data with numerous explanatory variables.

Conclusions:

  • BPL and BPEN offer novel approaches for variable selection in high-dimensional data.
  • These methods enhance the ability to retain theoretically important predictors.
  • The R package provides accessible tools for implementation.