Publicação
Bootstrap Resampling Method for Estimation of Fuzzy Regression Parameters and a Sample Application
| Resumo: | 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. |
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| Autores principais: | Topuz, Derviş |
| Outros Autores: | Özkaya, Volkan; Çiçek, Betül |
| Assunto: | bootstrapping fuzzines fuzzy linear regression mean prediction error fuzzy level fuzzy confidence interval |
| Ano: | 2024 |
| País: | portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | unknown |
| Instituição associada: | Instituto Nacional de Estatística |
| Idioma: | inglês |
| Origem: | REVSTAT-Statistical Journal |
| Resumo: | 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. |
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