Extended fuzzy linear models and least squares estimates

Fuzzy least-squares algorithms for interactive fuzzy linear regression models

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...

Linear Least Squares Estimates and Nonlinear Means

The consistency and asymptotic normality of a linear least squares estimate of the form (X,X)-X’Y when the mean is not X/I is investigated in this paper. The least squares estimate is a consistent estimate of the best linear approximation of the true mean function for the design chosen. The asymptotic normality of the least squares estimate depends on the design and the asymptotic mean may not ...

Least Squares Solutions of Inconsistent Fuzzy Linear Matrix Equations

Linear mixed models and penalized least squares

Linear mixed-effects models are an important class of statistical models that are not only used directly in many fields of applications but also used as iterative steps in fitting other types of mixed-effects models, such as generalized linear mixed models. The parameters in these models are typically estimated by maximum likelihood (ML) or restricted maximum likelihood (REML). In general there...

least squares solutions of inconsistent fuzzy linear matrix equations

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