Statistical regression analysis is a powerful and reliable method to determine the impact of
one or several independent variable (s) on a dependent variable. It is the most widely used …

In this paper, a new general approach is presented to fit a fuzzy regression model when the
response variable and the parameters of model are as fuzzy numbers. In this approach, for …

The varying coefficient models are powerful tools for exploring the dynamic pattern between
a response and a group of predictors in multiple regression models. In addition, robust …

In the present paper, a novel robust multiple regression model with fuzzy intercepts and non-
fuzzy regression coefficients was proposed. A two-stage robust procedure adopted with …

This paper extends the conventional semi-parametric partial linear regression model with
fuzzy predictors and fuzzy response in cases where 1—outliers occur in data set, 2 …

In the multivariate linear regression model, it is desirable to include the important
explanatory variables to achieve maximal prediction. In this context, the present paper is an …

In this paper, a new approach is presented to fit a fuzzy regression model with the fuzzy
coefficients when the explanatory variables and the response variable are as fuzzy …

Fuzzy multivariate regression analysis is aimed to model the relationship between a set of
fuzzy responses and a set of non-fuzzy or fuzzy explanatory variables. This paper extended …

The fuzzy varying coefficient regression model is a generalized version of fuzzy linear
regression model. This kind of model is flexible and adaptable than fuzzy linear regression …

Spline smoothing is a form of nonlinear regression when there is reason to believe that
relationship between the predictor and the response is curvilinear. In such cases, the spline …