1977Communication in Statistics- Theory and MethodsRequires access

Robustness properties for a simple class of rank estimates

Thomas P. Hettmansperger, Jessica Utts

Open publisher page 9 citations

Abstract

Robustness properties of a family of rank estimates to compete with trimmed means and other robust estimates for the one sample location problem are investigated. In particular, the influence curve and breakdown point are developed, as well as their finite sample equivalents, the sensitivity curve and tolerance. The estimates are formulated from a one sample rank test rather than the customary two sample rank test approach. In addition, a functional is implicitly defined for the asymptotic version of the estimate. Computational problems are considered and a simple iterative procedure for finding the estimate is given.

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What this paper is about

Robustness properties of a family of rank estimates to compete with trimmed means and other robust estimates for the one sample location problem are investigated. In particular, the influence curve and breakdown point are developed, as well as their finite sample equivalents, the sensitivity curve and tolerance. The estimates are formulated from a one sample rank test rather than the customary two sample rank test approach. In addition, a functional is implicitly defined for the asymptotic version of the estimate. Computational problems are considered and a simple iterative procedure for finding the estimate is given.

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Available abstract

Robustness properties of a family of rank estimates to compete with trimmed means and other robust estimates for the one sample location problem are investigated. In particular, the influence curve and breakdown point are developed, as well as their finite sample equivalents, the sensitivity curve and tolerance. The estimates are formulated from a one sample rank test rather than the customary two sample rank test approach. In addition, a functional is implicitly defined for the asymptotic version of the estimate. Computational problems are considered and a simple iterative procedure for finding the estimate is given.

Key concepts: Robustness (evolution), Mathematics, Rank (graph theory), Applied mathematics, Simple (philosophy), Sample (material), Mathematical optimization, Combinatorics

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