Rank-based inference for linear models: asymmetric errors

James C. Aubuchon and Thomas P. Hettmansperger

Statistics & Probability Letters, 1989, vol. 8, issue 2, 97-107

Abstract: In this paper robust, rank-based inference procedures are considered for general linear models with (possibly) asymmetric errors. Approximating standard errors of estimates and testing hypotheses about the model parameters require estimating a scaling functional, and an approach is developed which does not require symmetry of the underlying error distribution or replicates in the design matrix. In addition an estimate of the intercept is developed without requiring the assumption of a symmetric error distribution.

Keywords: inference; based; on; ranks; linear; models; asymmetric; errors; density; estimation (search for similar items in EconPapers)
Date: 1989
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Citations: View citations in EconPapers (3)

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