Singular majorants and minorants: enhanced design conditioning. Issue 9 (13th June 2017)

• Record Type: Journal Article
• Authors: Jensen, D. R.; Ramirez, D. E.
• Is Part Of: Journal of statistical computation and simulation. Volume 87:Issue 9(2017)
• Page Start: 1827
• Page End: 1841
• Publication Date: 2017-06-13
• Subjects: Conditioning -- ordering by majorization -- monotone functions -- efficiency indices -- design alterations -- non-uniquely optimal designs; 06A06 -- 15A45 -- 62J05; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2017.1289210
• Languages: English
• ISSNs: 0094-9655

Abstract

ABSTRACT: Lower and upper spectral bounds are known for matricesX ′ X ( k × k ) under Loewner [Uber monotone matrixfunktionen. Math Z. 1934;38:177–216] order, as are corresponding bounds for the factorX ( n × k ) under an induced order. Least upper bounds for the latter give designs with dominating Fisher Information, with consequent gains in linear inference; see Jensen DR, Ramirez DE [Enhanced design efficiency through least upper bounds. J Stat Comput Simul. 2016;86:1798–1817]. The present study examines properties on ordering the singular values of a design matrix using majorization as in Marshall and Olkin [Inequalities: theory of majorization and its applications. New York: Academic Press; 1979]. The principal focus includes conditioning through condition numbers, variance inflation factors, and lengths and efficiencies of OLS solutions. Functions monotone under the induced order are identified; equivalence classes of designs are displayed preserving a dispersion matrix or its eigenvalues; a minimal elementX m ( n × k ) is characterized; as are equivalence classes of( A, D, E ) -optimal designs showing the latter not to be unique. Algorithms to achieve enhanced designs are given on modifying a single design, or on amalgamating two designs, with essential consequences in linear inference. A collateral procedure, based on mixtures of Fisher information matrices, serves effectively to ameliorate the ill effects of near collinearity. Case studies illustrate gains to be made in practice, to include a substantial improvement in the analysis of classically ill-conditioned data from the literature.