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The aim is to assess confidence interval coverage of various shrinkage estimators in logistic regression with multicollinear predictors.

February 2, 2026StatsOpen Access

Improving Confidence Interval Estimation in Logistic Regression with Multicollinear Predictors: A Comparative Study of Shrinkage Estimators and Application to Prostate Cancer Data

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The aim is to assess confidence interval coverage of various shrinkage estimators in logistic regression with multicollinear predictors.
Used simulations to evaluate confidence interval coverage probability and interval width.
Compared shrinkage methods including ridge, Liu, and Kibria–Lukman.
Applied findings to a prostate cancer dataset for validation.
Shrinkage methods generally reduced interval width and mean squared error.
Many estimators struggled to maintain adequate confidence interval coverage.
Certain ridge and Kibria–Lukman estimators struck a balance between narrow width and consistent coverage.

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Comparative study evaluates shrinkage estimators for confidence intervals in prostate cancer data, indicating improved reliability.

Chowdhury et al. (2026) studied this question.

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