Robust Regression Diagnostics of Influential Observations in Linear Regression Model

• 作者: Adewale F. Lukman Kayode Ayinde Olatunji Arowolo
• 来源期刊: 统计学期刊(英文)
• 学科: 医学
• 关键词: DFFITS Cook’s D DFBETAS OLS RMSE
• 年，卷（期）: 2015,（4）
• 页码范围: 273-283
• 页数: 11页
• 分类号: R73

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.