Arbeitspapier

A methodology for automatised outlier detection in high-dimensional datasets: An application to euro area banks' supervisory data

Outlier detection in high-dimensional datasets poses new challenges that have not been investigated in the literature. In this paper, we present an integrated methodology for the identification of outliers which is suitable for datasets with higher number of variables than observations. Our method aims to utilise the entire relevant information present in a dataset to detect outliers in an automatized way, a feature that renders the method suitable for application in large dimensional datasets. Our proposed five-step procedure for regression outlier detection entails a robust selection stage of the most explicative variables, the estimation of a robust regression model based on the selected variables, and a criterion to identify outliers based on robust measures of the residuals' dispersion. The proposed procedure deals also with data redundancy and missing observations which may inhibit the statistical processing of the data due to the ill-conditioning of the covariance matrix. The method is validated in a simulation study and an application to actual supervisory data on banks' total assets.

ISBN
978-92-899-3276-9
Sprache
Englisch

Erschienen in
Series: ECB Working Paper ; No. 2171

Klassifikation
Wirtschaft
Methodological Issues: General
Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
Banks; Depository Institutions; Micro Finance Institutions; Mortgages
Thema
Outlier detection
Robust regression
Variable selection
High dimension
Missing data
Banking data

Ereignis
Geistige Schöpfung
(wer)
Farnè, Matteo
Vouldis, Angelos T.
Ereignis
Veröffentlichung
(wer)
European Central Bank (ECB)
(wo)
Frankfurt a. M.
(wann)
2018

DOI
doi:10.2866/357467
Handle
Letzte Aktualisierung
10.03.2025, 11:42 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • Farnè, Matteo
  • Vouldis, Angelos T.
  • European Central Bank (ECB)

Entstanden

  • 2018

Ähnliche Objekte (12)