eprintid: 3490
rev_number: 5
eprint_status: archive
userid: 6
dir: disk0/00/00/34/90
datestamp: 2016-05-23 11:46:35
lastmod: 2016-05-23 11:46:35
status_changed: 2016-05-23 11:46:35
type: article
metadata_visibility: show
creators_name: Bellio, Ruggero
creators_name: Coletto, Mauro
creators_id: 
creators_id: mauro.coletto@imtlucca.it
title: Simple outlier labeling based on quantile regression, with application to the steelmaking process
ispublished: pub
subjects: QA75
divisions: CSA
full_text_status: none
abstract: This paper introduces some methods for outlier identification in the regression setting, motivated by the analysis of steelmaking process data. The proposed methodology extends to the regression setting the boxplot rule, commonly used for outlier screening with univariate data. The focus here is on bivariate settings with a single covariate, but extensions are possible. The proposal is based on quantile regression, including an additional transformation parameter for selecting the best scale for linearity of the conditional quantiles. The resulting method is used to perform effective labeling of potential outliers, with a quite low computational complexity, allowing for simple implementation within statistical software as well as commonly used spreadsheets. Some simulation experiments have been carried out to study the swamping and masking properties of the proposal. The methodology is also illustrated by some real life examples, taking as the response variable the energy consumed in the melting process
date: 2015-03
publication: Applied Stochastic Models in Business and Industry
volume: 32
number: 2
publisher: Wiley
pagerange: 228-242
id_number: doi:10.1002/asmb.2146
refereed: TRUE
issn: 1524-1904,
official_url: http://doi.org/10.1002/asmb.2146
citation:   Bellio, Ruggero and Coletto, Mauro  Simple outlier labeling based on quantile regression, with application to the steelmaking process.  Applied Stochastic Models in Business and Industry, 32 (2).  pp. 228-242.  ISSN 1524-1904,      (2015)