Forthcoming

Bootstrap Resampling Method for Estimation of Fuzzy Regression Parameters and a Sample Application

Accepted - October 2022

Authors

Keywords:

bootstrapping, fuzzines, fuzzy linear regression, mean prediction error, fuzzy level, fuzzy confidence interval

Abstract

In fuzzy regression modeling, the fuzzy least squares technique is based on minimizing the squares of the total difference between observed and predicted outcomes. When the sample size is small, bootstrap resampling method is appropriate and useful for improving model estimation. The bootstrap resampling technique relies on resampling observations and resampling errors for bootstrap regression. The aim of this study is to investigate the use of Bootstrap in fuzzy regression modeling to estimate mean prediction with smaller errors at a particular α-segment, and apply it on a clinical data set. The behavior and properties of the least-squares estimators are affected when deviations or fuzziness arise in the sample and/or by slight changes in the data set. Bootstrap technique, on the other hand, provide robust estimators of the parameters which avoid such adverse effects.

Published

2022-10-13

How to Cite

Topuz, D., Özkaya, V. ., & Çiçek, B. (2022). Bootstrap Resampling Method for Estimation of Fuzzy Regression Parameters and a Sample Application: Accepted - October 2022. REVSTAT-Statistical Journal. Retrieved from https://revstat.ine.pt/index.php/REVSTAT/article/view/542

Issue

Section

Forthcoming Paper