An application of F-transform to a regression model based on Theil's method
Jin Hee Yoon, Hye-Young Jung, Seung Hoe Choi, Woojoo Lee · 2015
Regression Analysis is an analyzing method of regression model to explain the statistical relationship between explanatory variables and response variables. This paper propose a new regression analysis applying Theil's method based on F-transform. The main advantage of Theil's method in regression is the robustness, which means that it is not sensitive to outliers. The proposed method uses the median of rates of increments which are obtained from F-transform, based all possible pairs of F-transformed data in order to estimate the coefficients of fuzzy regression model. An example is given to show that the proposed regression analysis applying Theil's method based on F-transform is more robust than the least squares estimation (LSE) and even more robust than the original Theil's method.