Purpose of this paper is to investigate on the use of L_2 distance as a theoretical and practical estimation tool for parametric regression models. This approach is particularly helpful in all those situations involving the study of large data sets, handling large samples with a consistent numbers of outliers, situations in which maximum likelihood regression models are usually unstable. We shall also see how L_2 criterion may be applied in fitting mixture regression models and how it allows to detect clusters of data. After explaining the use of the methods with some simulated examples, we shall point out main results of an industrial case study.

Clusters Detection in Regression Models Applied to a Pollution Risk Evaluation Problem. The L_2 Approach.

ISAIA, Ennio Davide;DURIO, Alessandra
2006-01-01

Abstract

Purpose of this paper is to investigate on the use of L_2 distance as a theoretical and practical estimation tool for parametric regression models. This approach is particularly helpful in all those situations involving the study of large data sets, handling large samples with a consistent numbers of outliers, situations in which maximum likelihood regression models are usually unstable. We shall also see how L_2 criterion may be applied in fitting mixture regression models and how it allows to detect clusters of data. After explaining the use of the methods with some simulated examples, we shall point out main results of an industrial case study.
2006
6th European Network for Business and Industrial Statistics Conference (ENBIS06)
Wroclaw, Poland
18-20 september 2006
Proceedings of the 6th ENBIS Conference
ENBIS
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Mixed regression models; Robust regression; Similarity between densities
ISAIA E; DURIO A
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1432
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