Cellwise Robust M Regression

التفاصيل البيبلوغرافية
العنوان: Cellwise Robust M Regression
المؤلفون: Filzmoser, Peter, Höppner, Sebastiaan, Ortner, Irene, Serneels, Sven, Verdonck, Tim
المصدر: Computational Statistics and Data Analysis, 147 (2020), 106944
سنة النشر: 2019
المجموعة: Statistics
مصطلحات موضوعية: Statistics - Methodology
الوصف: The cellwise robust M regression estimator is introduced as the first estimator of its kind that intrinsically yields both a map of cellwise outliers consistent with the linear model, and a vector of regression coefficients that is robust against vertical outliers and leverage points. As a by-product, the method yields a weighted and imputed data set that contains estimates of what the values in cellwise outliers would need to amount to if they had fit the model. The method is illustrated to be equally robust as its casewise counterpart, MM regression. The cellwise regression method discards less information than any casewise robust estimator. Therefore, predictive power can be expected to be at least as good as casewise alternatives. These results are corroborated in a simulation study. Moreover, while the simulations show that predictive performance is at least on par with casewise methods if not better, an application to a data set consisting of compositions of Swiss nutrients, shows that in individual cases, CRM can achieve a significantly higher predictive accuracy compared to MM regression.
نوع الوثيقة: Working Paper
URL الوصول: http://arxiv.org/abs/1912.03407
رقم الأكسشن: edsarx.1912.03407
قاعدة البيانات: arXiv