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.

翻译：本文针对多重共线性条件下的模糊逻辑回归模型展开研究，该条件会导致参数估计的不稳定性和方差膨胀。在此模型中，响应变量和参数均表示为模糊三角数。为克服多重共线性问题，本文采用了多种Liu型估计器：模糊最大似然估计器（FMLE）、模糊逻辑岭估计器（FLRE）、模糊逻辑Liu估计器（FLLE）、模糊逻辑Liu型估计器（FLLTE）以及模糊逻辑Liu型参数估计器（FLLTPE）。通过不同样本量的模拟实验，并结合肾脏衰竭的真实模糊数据应用，使用均方误差（MSE）和拟合优度准则评估了模型性能。结果表明，与其他估计器相比，FLLTPE和FLLTE表现出更优越的性能。