In this paper, a new fuzzy density function, called N-density, is proposed. A real data set about Students Valuing Science with 5-fold cross-validation RMSE is conducted, for comparing the performances of the Choquet integral regression model with respect to six measures, Pmeasure, λ-measure, L-measure, extensional L-measure, completed L-measure and extensional completed L-measure based on this...

Processing an increasing volume of data, especially in industrial and manufacturing domains, calls for advanced tools of data analysis. Knowledge discovery is a process of analyzing data from different perspectives and summarizing the results into some useful and transparent findings. To address such challenges, a thorough extension and generalization of well-known techniques such as regression...

Spatial data analysis focuses on both attribute and locational information. Local analyses deal with differences across space whereas global analyses deal with similarities across space. This paper addresses an experimental comparative study to analyse the spatial data by some weighted local regression models. Five local regression models have been developed and their estimation capacities have...

a weighted linear regression model with impercise response and p-real explanatory variables is analyzed. the lr fuzzy random variable is introduced and a metric is suggested for coping with this kind of variables. a least square solution for estimating the parameters of the model is derived. the result are illustrated by the means of some case studies.

Regression analysis has become popular among several fields of research and standard tools in analysing data. This structure was represented by four commonly statistical models such as multiple linear regression, fuzzy linear regression (Tanaka, 1982), fuzzy linear regression (Ni, 2005) and extended fuzzy linear regression by benchmarking models under fuzziness (Chung, 2012). Colorectal cancer ...

The aim of this paper is to give a. sketch of a foundation of statistical analysis with fuzzy data, extending the methodology of Mathematical Stat,istics. For t.his purpose it will be discussed to equip the fuzzy sample spaces with suitable a-algebras and metrics. As the main application the parameter estimation in linear regression models with crisp parameters und fuzzy-valued variables will b...

This study presents a fuzzy varying coefficient regression model after deleting the outliers to improve the feasibility and effectiveness of the fuzzy regression model. The objective of our methodology is to allow the fuzzy regression coefficients to vary with a covariate, and simultaneously avoid the impact of data contaminated by outliers. In this paper, fuzzy regression coefficients are repr...

12:45 pm Lunch 02:30–03:30 pm A. COLUBI: Statistical inference about the means of fuzzy random variables: applications to the analysis of fuzzy-and real-valued data Estimation of a simple linear regression model for fuzzy random variables 04:00 pm Coffee break 04:30–05:00 pm A. WÜNSCHE: Asymptotic tests for the Aumann-expectation of fuzzy random variables 05:00–05:30 pm K. LENDELOVÁ, B. RIEČAN:...

earned value management is a critical project managementmethodology that evaluates project performance from cost andschedule viewpoints. the novel theoretical framework presented in thispaper estimates future performance of project regarding the past relativeinformation. it benefits from fuzzy regression (fr) models in estimationprocess in order to deal with the vagueness and impreciseness ofre...

since the pioneering work of zadeh, fuzzy set theory has been applied to amyriad of areas. song and chissom introduced the concept of fuzzy time series andapplied some methods to the enrolments of the university of alabama. thereafter weapply fuzzy techniques for system identification and apply statistical techniques tomodelling system. an automatic methodology framework that combines fuzzytech...