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 …

This research addresses the problem of restaurant locations ranking with applications for a
cosmopolitan big city like Kolkata, India. A restaurant selection is based on occasions …

Ridge regression model is a widely used model with many successful applications,
especially in managing correlated covariates in a multiple regression model …

Fuzzy regression analysis is aimed at modeling the relationship between a set of fuzzy
responses and a set of non-fuzzy/fuzzy predictors. However, compared to parametric …

In this paper, a new method is proposed for estimating fuzzy regression models based on a
novel robust support vector machines with exact predictors and fuzzy responses. For this …

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 …

Quantile regression estimates conditional quantiles and has found extensive applications in
real-life statistical procedures. This study assessed a new for nonlinear quantile regression …

This article proposes a novel asymmetric dual-regression model that combines the
principles of twin-support vector machine theory with the possibilistic regression analysis …

This paper proposed an extension for the classical partial univariate regression model with
non-fuzzy inputs and triangular fuzzy output. For this purpose, the popular non-parametric …

This paper aims to examine the impact of satisfaction with family, friends, school, and
government on life satisfaction, and to identify the impact of knowledge, attitude, and …