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 …

Purpose–The purpose of this paper is to critically review the latest approaches for capturing
and explaining heterogeneity in partial least squares (PLS) path modelling and to classify …

In recent years, deep learning caves out a research wave in machine learning. With
outstanding performance, more and more applications of deep learning in pattern …

An electrical discharge machine (EDM) has a high impact on production management, with
its process having many advantages over conventional machining processes, including the …

In order to estimate fuzzy regression models, possibilistic and least-squares procedures can
be considered. By taking into account a least-squares approach, regression models with …

Modeling a fuzzy-in fuzzy-out system where both inputs and outputs are uncertain is of
practical and theoretical importance. Fuzzy nonlinear regression (FNR) is one of the …

This study presents an integrated fuzzy regression and time series framework to estimate
and predict electricity demand for seasonal and monthly changes in electricity consumption …

In the early stage of product design, particularly for consumer products, affective design,
engineering, and marketing issues must be taken into considerationand they are commonly …

The success of new products depends greatly on customer satisfaction and meeting the
customer needs is vital for new product development. By incorporating customer needs in …

Trends in Fuzzy Statistics 1 Introduction Page 1 AUSTRIAN JOURNAL OF STATISTICS Volume
32 (2003), Number 3, 239-257 Trends in Fuzzy Statistics S. Mahmoud Taheri Isfahan University …