Outlier detection in interval data

• Type: Article in International Scientific Journal
• Date: 2018

Authors

Duarte Silva, APD (Author) Other; Filzmoser, P (Author) Other; brito, p (Author) FEP

Journal

Title: Advances in Data Analysis and Classification

Vol. 12

Pages: 785-822

ISSN: 1862-5347

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 7 Citations

Scopus - 7 Citations

Other information

Authenticus ID: P-00N-CRX

DOI: 10.1007/s11634-017-0305-y

Abstract (EN): A multivariate outlier detection method for interval data is proposed that makes use of a parametric approach to model the interval data. The trimmed maximum likelihood principle is adapted in order to robustly estimate the model parameters. A simulation study demonstrates the usefulness of the robust estimates for outlier detection, and new diagnostic plots allow gaining deeper insight into the structure of real world interval data.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 38