Projection pursuit algorithms to detect outliers

Maria Inés Stimolo,Pablo Arnaldo Ortiz

doi:10.11144/javeriana.cao33.ppado

Abstract

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.

Highlights

Databases o en show outliers observations, which present a different behavior from the majority
Outliers distort the results and mask the real data structure, so to detect them is an important task in the multivariate data analysis
Is work presented two pursuit algorithms to detect outliers, they are an example of the different algorithms available

Summary

Introduction

Databases o en show outliers observations, which present a different behavior from the majority. Sometimes the detection of outliers is the rst step in statistical analysis. The outliers need to be removed or downweighted; different causes motivate different procedures. E detection of outliers depends on the type of error (or cause) in the data. In the case of errors in measurement or data entry to the base, it is relatively simple to correct them and it is convenient to eliminate the obvious mistakes. A controversial question is: What should we do when the outliers derive from the intrinsic heterogeneity of the data?

Methods

Findings

Conclusion