Robustifying conventional outlier detection procedures


HEKİMOĞLU Ş.

JOURNAL OF SURVEYING ENGINEERING-ASCE, vol.125, no.2, pp.69-86, 1999 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 125 Issue: 2
  • Publication Date: 1999
  • Doi Number: 10.1061/(asce)0733-9453(1999)125:2(69)
  • Journal Name: JOURNAL OF SURVEYING ENGINEERING-ASCE
  • Journal Indexes: Science Citation Index Expanded, Scopus
  • Page Numbers: pp.69-86

Abstract

The conventional outlier detection procedures, such as the methods of Baarda and Pope or the t-testing procedure, determine only one outlier reliably. The approach to robustifying these procedures is as follows: (I) To identify outliers by using an estimator that has a high breakdown point and a bounded influence Function; (2) to find "good observations" by separating outliers from whole observations; (3) to constitute the reduced samples obtained by systematically adding each single outlier in turn to the good observations; and (4) to apply the conventional outlier detection procedures to each single reduced sample separately. To test the approach, an M-estimator with Andrews weight function is chosen. Then it is studied using a coordinate transformation simulation. Only two outliers are able to be determined reliably.