Open AccessProjection pursuit algorithms to detect outliers
Author(s) -
María Inés Stimolo,
Pablo Arnaldo Ortiz
Publication year - 2020
Publication title -
cuadernos de administración
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.109
H-Index - 9
ISSN - 1900-7205
DOI - 10.11144/javeriana.cao33.ppado
Subject(s) - outlier , projection pursuit , estimator , artificial intelligence , pattern recognition (psychology) , computer science , projection (relational algebra) , anomaly detection , multivariate statistics , set (abstract data type) , statistics , mathematics , data mining , algorithm , programming language
In this paper, we compare the methods proposed by Peña and Prieto (2001), and Filzmoser, Maronna, and Werner (2008) to detect outliers in a set of Argentine companies that quote their shares in the Stock Exchange. A significant heterogeneity between observations can be a consequence of the presence of outliers. The detection of outliers is an important task for the statistical analysis since they distort descriptive measures and parameters estimators. There are different multivariate methods to detect outliers, such as distance-based methods and projection pursuit methods.