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Wang, H.-D., Guo, S.-C. and Yue, L.-Z. (2014) An Approach to Fuzzy Multiple Linear Regression Mode Based on the Structured Element Theory. Systems Engineering—Theory & Practice, 34, 2628-2636.

has been cited by the following article:

• TITLE: Fuzzy Varying Coefficient Bilinear Regression of Yield Series

  AUTHORS: Ting He, Qiujun Lu

  KEYWORDS: Fuzzy Varying Coefficient Bilinear Regression Model, Fuzzy Financial Assets Yield, Least-Squares Method, Generalized Likelihood Ratio Test, Forecast

  JOURNAL NAME: Journal of Data Analysis and Information Processing, Vol.3 No.3, July 29, 2015

  ABSTRACT: We construct a fuzzy varying coefficient bilinear regression model to deal with the interval financial data and then adopt the least-squares method based on symmetric fuzzy number space. Firstly, we propose a varying coefficient model on the basis of the fuzzy bilinear regression model. Secondly, we develop the least-squares method according to the complete distance between fuzzy numbers to estimate the coefficients and test the adaptability of the proposed model by means of generalized likelihood ratio test with SSE composite index. Finally, mean square errors and mean absolutely errors are employed to evaluate and compare the fitting of fuzzy auto regression, fuzzy bilinear regression and fuzzy varying coefficient bilinear regression models, and also the forecasting of three models. Empirical analysis turns out that the proposed model has good fitting and forecasting accuracy with regard to other regression models for the capital market.