A robust Liu regression estimator. Issue 2 (7th February 2018)

• Record Type: Journal Article
• Authors: Filzmoser, Peter; Kurnaz, Fatma Sevinç
• Is Part Of: Communications in statistics. Volume 47:Issue 2(2018)
• Page Start: 432
• Page End: 443
• Publication Date: 2018-02-07
• Subjects: Liu estimator -- MM-estimates -- Partial least squares -- Partial robust M-estimator -- Robust estimator.; 62J07 -- 62J05 -- 62G35; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2016.1271889
• Languages: English
• ISSNs: 0361-0918

Abstract

ABSTRACT: The least-squares regression estimator can be very sensitive in the presence of multicollinearity and outliers in the data. We introduce a new robust estimator based on the MM estimator. By considering weights, also the resulting MM-Liu estimator is highly robust, but also the estimation of the biasing parameter is robustified. Also for high-dimensional data, a robust Liu-type estimator is introduced, based on the Partial Robust M-estimator. Simulation experiments and a real dataset show the advantages over the standard estimators and other robustness proposals.