ASYMPTOTIC CONFIDENCE INTERVALS BASED ON M-PROCEDURES IN ONE- AND TWO-SAMPLE MODELS

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概要

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• Asymptotic confidence intervals of location parameters are proposed in one- and two-sample models. These are robust procedures based on scale-invariant M-statistics. The one-sample procedures have the same robustness as Huber's M-estimators. Furthermore although the symmetry of the underlying distribution is needed in the asymptotic theory of Huber's M-estimators, the proposed procedures do not demand the symmetry in the two-sample model. The asymptotic efficiency of the proposed confidence intervals is given by a numerical integration.

• 一般社団法人日本統計学会の論文

著者

• Shiraishi Taka-aki

  Department Of Mathematical Sciences Yokohama City University

関連論文

• ONE-SAMPLE EXPLORATORY PROCEDURES AFTER SEARCHING THE UNDERLYING DISTRIBUTION
• MULTIPLE COMPARISONS BASED ON R-ESTIMATORS IN THE ONE-WAY LAYOUT
• ASYMPTOTIC CONFIDENCE INTERVALS BASED ON M-PROCEDURES IN ONE- AND TWO-SAMPLE MODELS

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