加权最小二乘与分位数回归求解简单线性回归异方差的比较

IF 1.1 Q3 STATISTICS & PROBABILITY
Welly Fransiska, S. Nugroho, R. Rachmawati
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引用次数: 0

摘要

回归分析是研究因变量与一个或多个自变量之间的关系。得到回归系数估计量最佳线性无偏估计量(BLUE)必须满足的一个重要假设是均方差。如果违背了同方差假设,则称之为异方差。异方差的结果是估计量保持线性和无偏,但它可能导致估计量没有最小方差,因此估计量不再是BLUE。本研究的目的是利用加权最小二乘法和分位数回归分析和解决异方差假设的违反。根据WLS和分位数回归的比较结果得出,本研究中用于克服异方差的最精确的方法是WLS方法,因为它产生的异方差更大(98%)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Comparison of Weighted Least Square and Quantile Regression for Solving Heteroscedasticity in Simple Linear Regression
Regression analysis is the study of the relationship between dependent variable and one or more independent variables. One of the important assumption that must be fulfilled to get the regression coefficient estimator Best Linear Unbiased Estimator (BLUE) is homoscedasticity. If the homoscedasticity assumption is violated then it is called heteroscedasticity. The consequences of heteroscedasticity are the estimator remain linear and unbiased, but it can cause estimator haven‘t a minimum variance so the estimator is no longer BLUE. The purpose of this study is to analyze and resolve the violation of heteroscedasticity assumption with Weighted Least Square(WLS) and Quantile Regression. Based on the results of the comparison between WLS and Quantile Regression obtained the most precise method used to overcome heteroscedasticity in this research is the WLS method because it produces that is greater (98%).
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来源期刊
CiteScore
2.00
自引率
15.40%
发文量
42
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