Exploratory tools for outlier detection in compositional data with structural zeros
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Autor:innen
Autor:in (Körperschaft)
Publikationsdatum
2017
Typ der Arbeit
Studiengang
Sammlung
Typ
01A - Beitrag in wissenschaftlicher Zeitschrift
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
Journal of Applied Statistics
Themenheft
DOI der Originalpublikation
Link
Reihe / Serie
Reihennummer
Jahrgang / Band
44
Ausgabe / Nummer
4
Seiten / Dauer
734-752
Patentnummer
Verlag / Herausgebende Institution
Taylor & Francis
Verlagsort / Veranstaltungsort
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
The analysis of compositional data using the log-ratio approach is based on ratios between the compositional parts. Zeros in the parts thus cause serious difficulties for the analysis. This is a particular prob- lem in case of structural zeros, which cannot be simply replaced by a non-zero value as it is done, e.g. for values below detection limit or missing values. Instead, zeros to be incorporated into further sta- tistical processing. The focus is on exploratory tools for identifying outliers in compositional data sets with structural zeros. For this pur- pose, Mahalanobis distances are estimated, computed either directly for subcompositions determined by their zero patterns, or by using imputation to improve the efficiency of the estimates, and then pro- ceed to the subcompositional and subgroup level. For this approach, new theory is formulated that allows to estimate covariances for imputed compositional data and to apply estimations on subgroups using parts of this covariance matrix. Moreover, the zero pattern structure is analyzed using principal component analysis for binary data to achieve a comprehensive view of the overall multivariate data structure. The proposed tools are applied to larger compositional data sets from official statistics, where the need for an appropriate treatment of zeros is obvious.
Schlagwörter
Fachgebiet (DDC)
Veranstaltung
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
Enddatum der Konferenz
Datum der letzten Prüfung
ISBN
ISSN
1360-0532
0266-4763
0266-4763
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Nein
Zukunftsfelder FHNW
Publikationsstatus
Veröffentlicht
Begutachtung
Peer-Review der ganzen Publikation
Open Access-Status
Closed
Lizenz
Zitation
Templ, M., Hron, K., & Filzmoser, P. (2017). Exploratory tools for outlier detection in compositional data with structural zeros. Journal of Applied Statistics, 44(4), 734–752. https://doi.org/10.1080/02664763.2016.1182135