On mitigating collinearity through mixtures. Issue 8 (24th May 2018)

• Record Type: Journal Article
• Authors: Jensen, D. R.; Ramirez, D. E.
• Is Part Of: Journal of statistical computation and simulation. Volume 88:Issue 8(2018)
• Page Start: 1437
• Page End: 1453
• Publication Date: 2018-05-24
• Subjects: Conditioning -- ordering by majorization -- monotone functions -- efficiency indices -- design modification; 62J05 -- 62J20; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2018.1437620
• Languages: English
• ISSNs: 0094-9655

Abstract

ABSTRACT: In linear models having near collinear columns of X, ridge and surrogate estimators often are used to mitigate collinearity. A new class of estimators is based on mixtures, either of X and a design minimal in an ordered class or of the Fisher information and a scalar matrix. Comparisons are drawn among choices for the mixing parameter, and the estimators are found to be admissible relative to ordinary least squares. Case studies demonstrate that selected mixture designs are perturbed from the original design to a lesser extent than are those of the surrogate method, while retaining reasonable efficiency characteristics.