J
Joseph W. McKean
Researcher at Western Michigan University
Publications - 110
Citations - 3362
Joseph W. McKean is an academic researcher from Western Michigan University. The author has contributed to research in topics: Linear model & Estimator. The author has an hindex of 28, co-authored 107 publications receiving 3106 citations. Previous affiliations of Joseph W. McKean include Pennsylvania State University.
Papers
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Book ChapterDOI
Iterated reweighted rank-based estimates for GEE models
TL;DR: In this article, a rank-based fitting procedure was proposed for regression coefficients, which only involves substituting a norm based on a score function for the Euclidean norm used by Liang and Zeger.
Journal ArticleDOI
Discriminant procedures based on efficient robust discriminant coordinates
TL;DR: In this paper, robust discriminant coordinates are obtained by the maximization of a Lawley-Hotelling test based on robust estimates, and the design matrix used in the fitting is the usual one-way incidence matrix of zeros and ones.
Journal ArticleDOI
On the robust rank analysis of linear models with nonsymmetric error distributions
TL;DR: In this article, a new estimate for the scale parameter is proposed and it is shown to be consistent for nonsymmetric and symmetric error distributions for linear models without assuming symmetry.
Journal ArticleDOI
Finite sample performance of tests for symmetry of the errors in a linear model
TL;DR: In this paper, the finite sample performance of a number of tests for symmetry of the distribution of the errors of a linear model is considered and the results of a power comparison among the tests are also discussed.
Journal ArticleDOI
Finite sample stability properties of the least median of squares estimator
TL;DR: The authors examined the stability properties of the least median of squares (LMS) estimate and showed that the LMS estimate has an influence function which is unbounded to the effects of centrally located x's and is not merely a consequence of the exact fit property and the curse of dimensionality.