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An Easy-to-Implement Hierarchical Standardization for Variable Selection under Strong Heredity Constraint.

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Summary
This summary is machine-generated.

We introduce hierarchical standardization to ensure regression models maintain strong heredity, crucial for variable selection. This effortless method works with any regression type and selection technique, showing performance comparable to existing standardization methods.

Keywords:
HeredityHierarchical StandardizationHierarchical structureMarginalityVariable Selection

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Area of Science:

  • Statistics
  • Machine Learning
  • Data Science

Background:

  • Regression models often require strong heredity (marginality), ensuring parent effects are included when interactions are present.
  • Current variable selection methods struggle to enforce strong heredity, often needing complex penalty functions or algorithms.

Purpose of the Study:

  • To propose a novel hierarchical standardization procedure for maintaining strong heredity in variable selection.
  • To offer an easily implementable method applicable to diverse regression models and selection techniques.

Main Methods:

  • Developed a hierarchical standardization procedure.
  • Applied the procedure to variable selection in regression analysis.
  • Conducted performance comparisons with regular standardization.

Main Results:

  • The proposed hierarchical standardization effectively maintains strong heredity.
  • The method is versatile, applicable to any regression type and variable selection algorithm.
  • Performance is comparable to traditional standardization methods.

Conclusions:

  • Hierarchical standardization offers an efficient and broadly applicable solution for enforcing strong heredity in variable selection.
  • The method's ease of implementation and comparable performance make it a valuable tool for statistical modeling.