加权最小二乘比(WLSR)方法对m估计量

M. Yazici
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引用次数: 2

摘要

回归分析是一种重要的统计工具,应用于许多科学领域。普通最小二乘是回归分析常用的一种方法。在回归分析中,最小二乘比值法优于普通最小二乘方法,特别是在存在异常值的情况下。本文提出了一种新的m估计方法,称为加权最小二乘比。本研究的目的是在建立回归模型的同时,确定在离群值和方差增加的情况下,哪种方法能给出更好的结果。根据回归参数估计值和相关值的平均绝对误差统计值,对加权最小二乘法和加权最小二乘法进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The weighted least squares ratio (WLSR) method to M-estimators
The regression analysis is a considerable statistical instrument applied in many sciences. The ordinary least squares is a conventional method used by Regression Analysis. In regression analysis, the least squares ratio method outperforms than the ordinary least squares method, especially in case of the presence of outliers. This paper includes a novel approach to M-estimators, named the weighted least squares ratio. The aim of this study is to determine which method gives better result in case of increasing outlier and variance while establishing a regression model. The weighted least squares and the weighted least squares ratio methods are compared according to statistics values of mean absolute errors of estimated the regression parameters and dependent value.
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