Comparison of Liu-Type Estimator for Multicollinearity in Fuzzy Logistic Regression Model

Authors

Ayad Habib Shemail, Department of Statistics, University of Wasit, Wasit, IraqFollow
Ahmed Razzaq Al-Lami, Department of Statistics, University of Wasit, Wasit, IraqFollow
Amal Hadi Rashid, Department of Statistics, University of Diyala, Diyala, IraqFollow

Abstract

This article addresses the fuzzy logistic regression model under conditions of multicollinearity, which causes instability and inflated variance in parameter estimation. In this model, both the response variable and parameters are represented as fuzzy triangular numbers. To overcome the multicollinearity problem, various Liu-type estimators were employed: Fuzzy Maximum Likelihood Estimators (FMLE), Fuzzy Logistic Ridge Estimators (FLRE), Fuzzy Logistic Liu Estimators (FLLE), Fuzzy Logistic Liu-type Estimators (FLLTE), and Fuzzy Logistic Liu-type Parameter Estimators (FLLTPE). Through simulations with various sample sizes and application to real fuzzy data on kidney failure, model performance was evaluated using mean square error (MSE) and goodness of fit criteria. Results demonstrated superior performance of FLLTPE and FLLTE compared to other estimators.

Recommended Citation

Shemail, Ayad Habib; Al-Lami, Ahmed Razzaq; and Rashid, Amal Hadi (2025) "Comparison of Liu-Type Estimator for Multicollinearity in Fuzzy Logistic Regression Model," Iraqi Journal for Computer Science and Mathematics: Vol. 6: Iss. 3, Article 44.
DOI: https://doi.org/10.52866/2788-7421.1311
Available at: https://ijcsm.researchcommons.org/ijcsm/vol6/iss3/44