Fuzzy regression discontinuity (RD) design and instrumental variable(s) (IV) regression share similar identification strategies and numerically yield the same results under certain conditions. While the weak identification problem is widely recognized in IV regressions, it has drawn much less attention in fuzzy RD designs, where the standard t-test can also suffer from asymptotic size distortio...

A new robust fuzzy regression clustering method is proposed. We estimate coefficients of a linear regression model in each unknown cluster. Our method aims to achieve robustness by trimming a fixed proportion of observations. Assignments to clusters are fuzzy: observations contribute to estimates in more than one single cluster. We describe general criteria for tuning the method. The proposed m...

Fuzzy regression analysis can be thought of as a fuzzy variation of classical regression analysis. It has been widely studied and applied in diverse areas. In general, the analysis of fuzzy regression models can be roughly divided into two categories. The 0rst is based on Tanaka’s linear-programming approach. The second category is based on the fuzzy least-squares approach. In this paper, new t...

Dichotomization of the outcome by a single cut-off point is an important part of various medical studies. Usually the relationship between the resulted dichotomized dependent variable and explanatory variables is analyzed with linear regression, probit regression or logistic regression. However, in many real-life situations, a certain cut-off point dividing the outcome into two groups is unknow...

this paper deals with ridge estimation of fuzzy nonparametric regression models using triangular fuzzy numbers. this estimation method is obtained by implementing ridge regression learning algorithm in the la- grangian dual space. the distance measure for fuzzy numbers that suggested by diamond is used and the local linear smoothing technique with the cross- validation procedure for selecting t...

Optimization of multi response surface (MRS) in robust designs is applied to determine optimum characteristics of a process in a satisfactory region and reduce variation of responses. In this paper, a methodology is proposed for optimizing multi response surface in robust designs, which optimizes mean and variance simultaneously by applying fuzzy set theory. At first, a method is proposed to co...

Nonparametric estimation capabilities of fuzzy systems in stochastic environments are analyzed in this paper. By using ideas from sieve estimation, increasing sequences of fuzzy rule-based systems, capable of consistently estimating regression surfaces in different settings, are constructed. Results include least squares learning of a mapping perturbed by additive random noise in a static-regre...