Title :
Likelihood inference based on fuzzy data in regression model
Author :
Hye-Young Jung ; Woo-Joo Lee ; Jin Hee Yoon ; Seung Hoe Choi
Author_Institution :
Dept. of Stat., Seoul Nat. Univ., Seoul, South Korea
Abstract :
In regression analysis, such as other statistical inference problems, imprecise data may be encountered. In this paper, we focused on some statistical inferences in fuzzy regression model on the basis of information the supplied by the available fuzzy data based on imprecise data. For these, we consider the maximum likelihood estimates of linear regression parameters based on fuzzy data for the variety of membership functions. Numerical example is given for estimating the regression parameters in order to provide an illustration of the proposed maximum likelihood estimation.
Keywords :
data handling; fuzzy set theory; inference mechanisms; regression analysis; fuzzy data; fuzzy regression model; imprecise data; likelihood inference; linear regression parameters; maximum likelihood estimation; statistical inferences; Carbon; Computational modeling; Data models; Linear regression; Mathematical model; Maximum likelihood estimation; Vectors;
Conference_Titel :
Soft Computing and Intelligent Systems (SCIS), 2014 Joint 7th International Conference on and Advanced Intelligent Systems (ISIS), 15th International Symposium on
DOI :
10.1109/SCIS-ISIS.2014.7044744