| Publication type: | Article in scientific journal |
| Type of review: | Peer review (publication) |
| Title: | Exploratory tools for outlier detection in compositional data with structural zeros |
| Authors: | Templ, Matthias Hron, K. Filzmoser, P. |
| DOI: | 10.1080/02664763.2016.1182135 |
| Published in: | Journal of Applied Statistics |
| Volume(Issue): | 44 |
| Issue: | 4 |
| Page(s): | 734 |
| Pages to: | 752 |
| Issue Date: | 2016 |
| Publisher / Ed. Institution: | Taylor & Francis |
| ISSN: | 0266-4763 1360-0532 |
| Language: | English |
| Subject (DDC): | 510: Mathematics |
| Abstract: | 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 problem 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 statistical processing. The focus is on exploratory tools for identifying outliers in compositional data sets with structural zeros. For this purpose, 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 proceed 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. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/5693 |
| Fulltext version: | Published version |
| License (according to publishing contract): | Licence according to publishing contract |
| Departement: | School of Engineering |
| Organisational Unit: | Institute of Data Analysis and Process Design (IDP) |
| Appears in collections: | Publikationen School of Engineering |
Files in This Item:
There are no files associated with this item.
Show full item record
Templ, M., Hron, K., & Filzmoser, P. (2016). 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
Templ, M., Hron, K. and Filzmoser, P. (2016) ‘Exploratory tools for outlier detection in compositional data with structural zeros’, Journal of Applied Statistics, 44(4), pp. 734–752. Available at: https://doi.org/10.1080/02664763.2016.1182135.
M. Templ, K. Hron, and P. Filzmoser, “Exploratory tools for outlier detection in compositional data with structural zeros,” Journal of Applied Statistics, vol. 44, no. 4, pp. 734–752, 2016, doi: 10.1080/02664763.2016.1182135.
TEMPL, Matthias, K. HRON und P. FILZMOSER, 2016. Exploratory tools for outlier detection in compositional data with structural zeros. Journal of Applied Statistics. 2016. Bd. 44, Nr. 4, S. 734–752. DOI 10.1080/02664763.2016.1182135
Templ, Matthias, K. Hron, and P. Filzmoser. 2016. “Exploratory Tools for Outlier Detection in Compositional Data with Structural Zeros.” Journal of Applied Statistics 44 (4): 734–52. https://doi.org/10.1080/02664763.2016.1182135.
Templ, Matthias, et al. “Exploratory Tools for Outlier Detection in Compositional Data with Structural Zeros.” Journal of Applied Statistics, vol. 44, no. 4, 2016, pp. 734–52, https://doi.org/10.1080/02664763.2016.1182135.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.