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Outlier Identification Via Non-parametric Expectile-order

Record type: Monograph (Project Report)

Non-parametric conditional expectiles can be used to characterise the relationship between a response variable and explanatory variables when the behaviour of “outlier” individuals is of interest. The aim of this paper is to demonstrate how an individual’s expectile-order, based on a non-parametric approximation to the expectile regression function, can be used as a measure of the extremity of that individual’s value relative to the values of other members of a dataset. In particular, we propose five estimators of this expectile-order which we compare via simulation. The use of expectile-order to characterise outliers or extreme values is then illustrated using a dataset of prescribing behaviour by physicians in the Midi-Pyrenees region of France during 1999. THIS DOCUMENT HAS BEEN SUPERSEDED BY M05/01.

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Citation

Aragon, Yves, Chambers, Ray, Leconte, Eve and Poiraud-Casanova, Sandrine (2003) Outlier Identification Via Non-parametric Expectile-order , Southampton, UK Southampton Statistical Sciences Research Institute 23pp. (S3RI Methodology Working Papers, M03/12).

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Published date: 2003

Identifiers

Local EPrints ID: 8162
URI: http://eprints.soton.ac.uk/id/eprint/8162
PURE UUID: 97b850f1-eaae-480c-9767-8e38f1b0627c

Catalogue record

Date deposited: 11 Jul 2004
Last modified: 17 Jul 2017 17:13

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Contributors

Author: Yves Aragon
Author: Ray Chambers
Author: Eve Leconte
Author: Sandrine Poiraud-Casanova

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