文章基本信息

标题：Robust Regression Diagnostics of Influential Observations in Linear Regression Model
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作者：Kayode Ayinde 1 , Adewale F. Lukman 1 , Olatunji Arowolo
期刊名称：Open Journal of Statistics
印刷版ISSN：2161-718X
电子版ISSN：2161-7198
出版年度：2015
卷号：05
期号：04
页码：273-283
DOI：10.4236/ojs.2015.54029
语种：English
出版社：Scientific Research Publishing
摘要：In regression analysis, data sets often contain unusual observations called outliers. Detecting these unusual observations is an important aspect of model building in that they have to be diagnosed so as to ascertain whether they are influential or not. Different influential statistics including Cook’s Distance, Welsch-Kuh distance and DFBETAS have been proposed. Based on these influential statistics, the use of some robust estimators MM, Least trimmed square (LTS) and S is proposed and considered as alternative to influential statistics based on the robust estimator M and the ordinary least square (OLS). The statistics based on these estimators were applied into three set of data and the root mean square error (RMSE) was used as a criterion to compare the estimators. Generally, influential measures are mostly efficient with M or MM robust estimators.
关键词：DFFITS; Cook’s D; DFBETAS; OLS; RMSE