Abstract
In this paper, the fuzzy semi-parametric logistic quantile regression model was studied in the absence of special conditions in the classical regression models. This model becomes more flexible to deal with data for outlier's values and in the absence of a linear regression condition when the data of the dependent variable are restricted and fuzzy data and some of the variables are nonparametric, and the dependent variable in this model represents the fuzzy triangular number.
 The estimation of the semi-parametric logistic quantile regression model has been applied to simulated data when the sample size is (25, 50, 75, 100) and with a repetition of 1000. Where the model is estimated in two steps, the first step is to estimate the parametric part, and the second step is to estimate the non-parametric part by the Nadaraya-Watson estimator through different kernel functions. Depending on the mean squares error and the measure of goodness of fit, the results indicated the best estimate of a model with the Kernel-Cassian function when the quantile of the fuzzy conditional distribution equals 0.2 for all sample sizes .
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