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首页> 外文期刊>Journal of Modern Applied Statistical Methods >An Alternative Method for Multiple Linear Model Regression Modeling, a Technical Combining of Robust, Bootstrap and Fuzzy Approach
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An Alternative Method for Multiple Linear Model Regression Modeling, a Technical Combining of Robust, Bootstrap and Fuzzy Approach

机译:一种替代方法,用于多个线性模型回归建模,鲁棒,引导和模糊方法的技术结合

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摘要

Research on modeling is becoming popular nowadays, there are several of analyses used in research for modeling and one of them is known as applied multiple linear regressions (MLR). To obtain a bootstrap, robust and fuzzy multiple linear regressions, an experienced researchers should be aware the correct method of statistical analysis in order to get a better improved result. The main idea of bootstrapping is to approximate the entire sampling distribution of some estimator. To achieve this is by resampling from our original sample. In this paper, we emphasized on combining and modeling using bootstrapping, robust and fuzzy regression methodology. An algorithm for combining method is given by SAS language. We also provided some technical example of application of method discussed by using SAS computer software. The visualizing output of the analysis is discussed in detail.
机译:现在研究建模正在变得流行,在对建模的研究中使用了几个分析,其中一个被称为应用多个线性回归(MLR)。为了获得引导,鲁棒和模糊多元线性回归,经验丰富的研究人员应该了解统计分析的正确方法,以获得更好的改进结果。引导的主要概念是近似一些估计器的整个采样分布。为实现这一目标是通过从我们原来的样本重新采样。在本文中,我们强调了使用自举,鲁棒和模糊回归方法的组合和建模。 SAS语言给出了一种组合方法的算法。我们还提供了一些通过使用SAS计算机软件讨论的方法的应用示例。详细讨论了分析的可视化输出。

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