Asymptotic Confidence Intervals Based on M-procedures in One- and Two-sample Models

• Shiraishi Taka-aki

  Department of Mathematical Sciences, Yokohama City University

Abstract

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.

Journal

• JOURNAL OF THE JAPAN STATISTICAL SOCIETY

  JOURNAL OF THE JAPAN STATISTICAL SOCIETY 34 (1), 87-99, 2004

  THE JAPAN STATISTICAL SOCIETY

Keywords

• asymptotics
• confidence region
• M-estimators
• robustness

Details

• CRID: 1390001205285492352
• NII Article ID: 110003144474
• NII Book ID: AA1105098X
• DOI: 10.14490/jjss.34.87
• ISSN: 13486365
• ISSN: 18822754
• MRID: 2084062
• NDL BIB ID: 7042925
• Web Site: http://id.ndl.go.jp/bib/7042925
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I7042925
• Web Site: https://www.jstage.jst.go.jp/article/jjss/34/1/34_1_87/_pdf
• Text Lang: en
• Data Source:
  • JaLC
  • NDL
  • Crossref
  • CiNii Articles
• Abstract License Flag: Disallowed