Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Logistic regression when binary predictor variables are highly correlated.

L Barker1, C Brown

  • 1National Immunization Program, Centers for Disease Control and Prevention, 1600 Clifton Ave, MS E62, Atlanta, GA 30033, USA. isb8@cdc.gov

Statistics in Medicine
|May 9, 2001
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

The association between depressive symptoms and cigarette smoking in an urban primary care sample.

International journal of psychiatry in medicine·2000
Same author

Cytological findings in a mucin-secreting follicular carcinoma of the thyroid.

Cytopathology : official journal of the British Society for Clinical Cytology·2000
Same author

Identifying agent(s) associated with poult enteritis mortality syndrome: importance of the thymus.

Avian diseases·2000
Same author

Primary care patients' opinions regarding the importance of various aspects of care for depression.

General hospital psychiatry·2000
Same author

Daily smoking by Louisiana students: sixth through twelfth grades, 1996-1997.

Southern medical journal·2000
Same author

A genome-wide search for susceptibility loci to human essential hypertension.

Hypertension (Dallas, Tex. : 1979)·2000
Same journal

Interpretable Bayesian Modeling for Multireader Multicase Studies: Addressing Overdispersion and Limited Sample Size in Diagnostic Enhancement Evaluation.

Statistics in medicine·2026
Same journal

Adaptive Sequential Multiple Hypotheses Testing for Concomitant Vaccine Safety Surveillance.

Statistics in medicine·2026
Same journal

Novel Distance Regression for Repeated Outcomes With Missing Data: Applications to Longitudinal and Crossover Studies of Microbiome Beta-Diversity.

Statistics in medicine·2026
Same journal

Optimal Weighted Tests for Replication Studies and the 'Two-Trials Rule' With Multiple Hypotheses.

Statistics in medicine·2026
Same journal

Identifiable Copula-Double-Cox Models: A Fully Parametric Framework for Dependent Right-Censored Survival Data.

Statistics in medicine·2026
Same journal

Moving From Individualized Risk-Based Prevention to Benefit-Based Prevention: Estimating Individualized Life-Years Gained From Prevention Services as a Basis for Eligibility.

Statistics in medicine·2026
See all related articles

Ridge and principal components logistic regression offer improved estimates over standard logistic regression when dealing with multicollinearity, reducing mean square error for better statistical modeling.

Area of Science:

  • Statistics
  • Biostatistics
  • Econometrics

Background:

  • Standard logistic regression can yield unreliable estimates when predictor variables are highly correlated (multicollinear).
  • Multicollinearity inflates the mean square error of regression coefficients.
  • Existing methods like ridge regression and principal components regression address multicollinearity in ordinary least squares regression.

Purpose of the Study:

  • To introduce and evaluate generalizations of ridge regression and principal components regression within the logistic regression framework.
  • To demonstrate that these generalized methods can reduce the mean square error compared to standard logistic regression.
  • To provide guidance on selecting the appropriate logistic regression method.

Main Methods:

Related Experiment Videos

  • Application of ridge regression principles to logistic regression.
  • Application of principal components regression principles to logistic regression.
  • Comparison of mean square error for estimates from standard, ridge, and principal components logistic regression.

    • Main Results:

    • Estimates from ridge logistic regression and principal components logistic regression exhibit smaller mean square error than those from standard logistic regression in the presence of multicollinearity.
    • These advanced methods effectively mitigate the adverse effects of multicollinearity on logistic regression models.
    • The study provides a quantitative basis for choosing between the methods.

    Conclusions:

    • Ridge and principal components logistic regression are valuable alternatives to standard logistic regression when multicollinearity is present.
    • These methods lead to more stable and reliable parameter estimates.
    • Recommendations are provided to aid practitioners in selecting the most suitable logistic regression technique based on data characteristics.