Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Templ, Matthias | - |
| dc.date.accessioned | 2021-03-14T11:40:22Z | - |
| dc.date.available | 2021-03-14T11:40:22Z | - |
| dc.date.issued | 2020 | - |
| dc.identifier.uri | https://uros.hopto.org/ | de_CH |
| dc.identifier.uri | https://digitalcollection.zhaw.ch/handle/11475/22002 | - |
| dc.description | Keynote | de_CH |
| dc.description.abstract | Probability density functions are frequently used to characterize the distributional properties of large-scale database systems. As functional compositions, densities carry primarily relative information. As such, standard methods of functional data analysis (FDA) are not appropriate for their statistical processing and thus a compositional alternative is proposed. The aim of this presentation is to outline a concise methodology for functional principal component analysis of densities based on the geometry of the Bayes space B2 of functional compositions. Advances of the proposed approach are demonstrated using a real-world example of population pyramids in Upper Austria. For compositional analysis we also introduce the R package robCompositions. | de_CH |
| dc.language.iso | en | de_CH |
| dc.publisher | Statistik Austria | de_CH |
| dc.rights | Licence according to publishing contract | de_CH |
| dc.subject | Functional data analysis | de_CH |
| dc.subject | Compositional data analysis | de_CH |
| dc.subject | Probability density functions | de_CH |
| dc.subject.ddc | 005: Computerprogrammierung, Programme und Daten | de_CH |
| dc.title | Functional data analysis in Bayes spaces with an application to spatio-temporal population data | de_CH |
| dc.type | Konferenz: Sonstiges | de_CH |
| dcterms.type | Text | de_CH |
| zhaw.departement | School of Engineering | de_CH |
| zhaw.organisationalunit | Institut für Datenanalyse und Prozessdesign (IDP) | de_CH |
| zhaw.conference.details | Use of R in Official Statistics 2020 : 8th international conference, virtual, 2-4 December 2020 | de_CH |
| zhaw.funding.eu | No | de_CH |
| zhaw.originated.zhaw | Yes | de_CH |
| zhaw.publication.status | publishedVersion | de_CH |
| zhaw.publication.review | Not specified | de_CH |
| zhaw.title.proceedings | uRos 2020 Abstracts | de_CH |
| zhaw.webfeed | Statistik und Quantitative Finance | de_CH |
| zhaw.author.additional | No | de_CH |
| zhaw.display.portrait | Yes | de_CH |
| Appears in collections: | Publikationen School of Engineering | |
Files in This Item:
There are no files associated with this item.
Show simple item record
Templ, M. (2020). Functional data analysis in Bayes spaces with an application to spatio-temporal population data. uRos 2020 Abstracts. https://uros.hopto.org/
Templ, M. (2020) ‘Functional data analysis in Bayes spaces with an application to spatio-temporal population data’, in uRos 2020 Abstracts. Statistik Austria. Available at: https://uros.hopto.org/.
M. Templ, “Functional data analysis in Bayes spaces with an application to spatio-temporal population data,” in uRos 2020 Abstracts, 2020. [Online]. Available: https://uros.hopto.org/
TEMPL, Matthias, 2020. Functional data analysis in Bayes spaces with an application to spatio-temporal population data. In: uRos 2020 Abstracts [online]. Conference presentation. Statistik Austria. 2020. Verfügbar unter: https://uros.hopto.org/
Templ, Matthias. 2020. “Functional Data Analysis in Bayes Spaces with an Application to Spatio-Temporal Population Data.” Conference presentation. In uRos 2020 Abstracts. Statistik Austria. https://uros.hopto.org/.
Templ, Matthias. “Functional Data Analysis in Bayes Spaces with an Application to Spatio-Temporal Population Data.” uRos 2020 Abstracts, Statistik Austria, 2020, https://uros.hopto.org/.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.