nach oben

Advances in Data Analysis and Classification

Erschienen in:

15.12.2017 | Regular Article

Outlier detection in interval data

verfasst von: A. Pedro Duarte Silva, Peter Filzmoser, Paula Brito

Erschienen in: Advances in Data Analysis and Classification | Ausgabe 3/2018

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

A multivariate outlier detection method for interval data is proposed that makes use of a parametric approach to model the interval data. The trimmed maximum likelihood principle is adapted in order to robustly estimate the model parameters. A simulation study demonstrates the usefulness of the robust estimates for outlier detection, and new diagnostic plots allow gaining deeper insight into the structure of real world interval data.

Vorheriger Artikel Non-symmetrical composite-based path modeling

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

Billard B, Diday E (2003) From the statistics of data to the statistics of knowledge: symbolic data analysis. J Am Stat Assoc 98(462):470–487MathSciNetCrossRef

Bock H-H, Diday E (2000) Analysis of symbolic data. Exploratory methods for extracting statistical information from complex data. Springer, HeidelbergMATH

Brito P (2014) Symbolic data analysis: another look at the interaction of data mining and statistics. WIREs Data Min Knowl Discov 4(4):281–295CrossRef

Brito P, Duarte Silva AP (2012) Modelling interval data with Normal and Skew-Normal distributions. J Appl Stat 39(1):3–20MathSciNetCrossRef

Cerioli A (2010) Multivariate outlier detection with high-breakdown estimators. J Am Stat Assoc 105(489):147–156MathSciNetCrossRef

De Carvalho FAT, Brito P, Bock H-H (2006) Dynamic clustering for interval data based on \(L_2\) distance. Comput Stat 21(2):231–250MathSciNetCrossRef

De Carvalho FAT, Lechevallier Y (2009) Partitional clustering algorithms for symbolic interval data based on single adaptive distances. Pattern Recogn 42(7):1223–1236CrossRef

Dias S, Brito P (2017) Off the beaten track: a new linear model for interval data. Eur J Oper Res 258(3):1118–1130MathSciNetCrossRef

Diday E, Noirhomme-Fraiture M (2008) Symbolic data analysis and the SODAS software. Wiley, ChichesterMATH

Douzal-Chouakria A, Billard L, Diday E (2011) Principal component analysis for interval-valued observations. Stat Anal Data Min 4(2):229–246MathSciNetCrossRef

Duarte Silva AP, Brito P (2017) MAINT.DATA: Model and analyze interval data. R Package,version 1.2.0. http://cran.r-project.org/web/packages/MAINT.Data/index.html

Duarte Silva AP, Brito P (2015) Discriminant analysis of interval data: an assessment of parametric and distance-based approaches. J Classif 32(3):516–541MathSciNetCrossRef

Filzmoser P (2004) A multivariate outlier detection method. In: S. Aivazian, P. Filzmoser and Yu. Kharin, editors, In Proceedings of the 7th international conference on computer data analysis and modeling, vol 1, 18–22, Belarusian State University, Minsk

Filzmoser P, Reimann C, Garrett RG (2005) Multivariate outlier detection in exploration geochemistry. Comput Geosci 31:579–587CrossRef

Hadi AS, Luceño A (1997) Maximum trimmed likelihood estimators: a unified approach, examples, and algorithms. Comput Stat Data Anal 25(3):251–272MathSciNetCrossRef

Hardin J, Rocke DM (2005) The distribution of robust distances. J Comput Gr Stat 14:910–927MathSciNetCrossRef

Hubert M, Rousseeuw PJ, Van Aelst S (2008) High-breakdown robust multivariate methods. Stat Sci 23(1):92–119MathSciNetCrossRef

Korkmaz S, Goksuluk D, Zararsiz G (2014) MVN: an R package for assessing multivariate normality. R J 6(2):151–162

Le-Rademacher J, Billard L (2011) Likelihood functions and some maximum likelihood estimators for symbolic data. J Stat Plan Inference 141:1593–1602MathSciNetCrossRef

Le-Rademacher J, Billard L (2012) Symbolic covariance principal component analysis and visualization for interval-valued data. J Comput Gr Stat 21(2):413–432MathSciNetCrossRef

Li S, Lee R, Lang S-D (2006) Detecting outliers in interval data. In Proceedings of the 44th annual southeast regional conference, ACM, pp 290–295

Lima Neto E, De Carvalho FAT (2008) Centre and range method for fitting a linear regression model to symbolic interval data. Comput Stat Data Anal 52(3):1500–1515MathSciNetCrossRef

Lima Neto E, De Carvalho FAT (2010) Constrained linear regression models for symbolic interval-valued variables. Comput Stat Data Anal 54(2):333–347MathSciNetCrossRef

Lima Neto E, Cordeiro GM, De Carvalho FAT (2011) Bivariate symbolic regression models for interval-valued variables. J Stat Comput Simul 81(11):1727–1744MathSciNetCrossRef

Neykov N, Filzmoser P, Dimova R, Neytchev P (2007) Robust fitting of mixtures using the trimmed likelihood estimator. Comput Stat Data Anal 52(1):299–308MathSciNetCrossRef

Neykov NM, Müller CH (2003) Breakdown point and computation of trimmed likelihood estimators in generalized linear models. In: Dutter R, Filzmoser P, Gather U, Rousseeuw PJ (eds) Developments in robust statistics. Physica-Verlag, Heidelberg, pp 277–286CrossRef

Noirhomme-Fraiture M, Brito P (2011) Far beyond the classical data models: symbolic data analysis. Stat Anal Data Min 4(2):157–170MathSciNetCrossRef

Pison G, Van Aelst S, Willems G (2002) Small sample corrections for LTS and MCD. Metrika 55(1–2):111–123MathSciNetCrossRef

Ramos-Guajardo AB, Grzegorzewski P (2016) Distance-based linear discriminant analysis for interval-valued data. Inf Sci 372:591–607CrossRef

Rousseeuw PJ (1984) Least median of squares regression. J Am Stat Assoc 79(388):871–880MathSciNetCrossRef

Rousseeuw PJ (1985) Multivariate estimation with high breakdown point. Math Stat Appl 8:283–297MathSciNetCrossRef

Rousseeuw PJ, Van Driessen K (1999) A fast algorithm for the minimum covariance determinant estimator. Technometrics 41(3):212–223CrossRef

Rousseeuw PJ, Van Zomeren BC (1990) Unmasking multivariate outliers and leverage points. J Am Stat Assoc 85(411):633–639CrossRef

Van Rijsbergen CJ (1979) Information retrieval, 2nd edn. Butterworth, LondonMATH

Vandev DL, Neykov NM (1998) About regression estimators with high breakdown point. Statistics 32:111–129MathSciNetCrossRef

Viattchenin D (2012) Detecting outliers in interval-valued data using heuristic possibilistic clustering. J Comput Sci Control Syst 5(2):39–44

Titel: Outlier detection in interval data
verfasst von: A. Pedro Duarte Silva
Peter Filzmoser
Paula Brito
Publikationsdatum: 15.12.2017
Verlag: Springer Berlin Heidelberg
Erschienen in: Advances in Data Analysis and Classification / Ausgabe 3/2018
Print ISSN: 1862-5347
Elektronische ISSN: 1862-5355
DOI: https://doi.org/10.1007/s11634-017-0305-y

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 3/2018

Editorial for issue 3/2018

A divisive clustering method for functional data with special consideration of outliers

Non-symmetrical composite-based path modeling

Cluster-based sparse topical coding for topic mining and document clustering

Rethinking an ROC partial area index for evaluating the classification performance at a high specificity range

Mutual information, phi-squared and model-based co-clustering for contingency tables