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Rank-based inference for linear models: asymmetric errors

Author

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  • Aubuchon, James C.
  • Hettmansperger, Thomas P.

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.

Suggested Citation

  • Aubuchon, James C. & Hettmansperger, Thomas P., 1989. "Rank-based inference for linear models: asymmetric errors," Statistics & Probability Letters, Elsevier, vol. 8(2), pages 97-107, June.
    • Handle: RePEc:eee:stapro:v:8:y:1989:i:2:p:97-107
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    Cited by:

    1. Omelka, Marek, 2008. "Comparison of two types of confidence intervals based on Wilcoxon-type R-estimators," Statistics & Probability Letters, Elsevier, vol. 78(18), pages 3366-3372, December.
    2. Zhan, Xiaojiang & Hettmansperger, Thomas P., 2007. "Bayesian R-estimates in two-sample location models," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 5077-5089, June.
    3. Feng, Long & Zou, Changliang & Wang, Zhaojun, 2012. "Rank-based inference for the single-index model," Statistics & Probability Letters, Elsevier, vol. 82(3), pages 535-541.

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