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Conditional local influence in case-weights linear regression.

W Y Poon1, Y S Poon

  • 1Department of Statistics, Chinese University of Hong Kong, Shatin, Hong Kong. wypoon@cuhk.edu.hk

The British Journal of Mathematical and Statistical Psychology
|June 8, 2001
PubMed
Summary

This study introduces a new measure for conditional local influence in statistical models. It enhances the detection of masking effects in linear regression, revealing new data insights.

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Area of Science:

  • Statistical modeling
  • Regression analysis
  • Influence diagnostics

Background:

  • Cook's local influence approach uses normal curvature to assess model perturbations.
  • It identifies joint masking effects but not conditional ones.
  • Existing methods struggle with the nuanced nature of conditional masking.

Purpose of the Study:

  • To develop a novel measure for detecting conditional local influences.
  • To extend the capabilities of local influence diagnostics in statistical modeling.
  • To address limitations in identifying specific types of masking effects.

Main Methods:

  • Construction of a new measure for conditional local influence.
  • Application and illustration using the linear regression model.

Related Experiment Videos

  • Validation with several reported data sets.

    • Main Results:

    • The proposed measure effectively defines and detects conditional local influences.
    • It provides information distinct from Cook's distance in linear regression.
    • New insights into data structure and masking effects were revealed.

    Conclusions:

    • The new measure enhances the identification of conditional masking effects.
    • It offers a valuable tool for robust statistical analysis.
    • The approach demonstrates improved diagnostic capabilities for regression models.