Statistical Analysis of Bivariate Densities with Compositional Splines

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F25%3A73634021" target="_blank" >RIV/61989592:15310/25:73634021 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-031-92383-8_60" target="_blank" >http://dx.doi.org/10.1007/978-3-031-92383-8_60</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-92383-8_60" target="_blank" >10.1007/978-3-031-92383-8_60</a>

Result language
angličtina
Original language name
Statistical Analysis of Bivariate Densities with Compositional Splines
Original language description
In general, densities occur naturally as data as a result of massive data aggregation. Thus, their suitable approximation for reliable (functional) data analysis is crucial not only for the univariate case but also for multivariate in general. However, their specific features, i.e. scale invariance and relative scale, have to be taken into account to build a suitable spline basis. This can be achieved using Bayes space methodology, which also allows to decompose densities into their interactive and independent part, which offers a powerful tool for the study of the dependence structure between random variables. Therefore, it is desirable for the spline approximation to admit the same decomposition. In addition, Bayes spaces methodology enables to convert densities to a standard Lebesgue space of square integrable functions using centered log-ratio transformation in order to use standard operations of the Lebesgue space, where, as a consequence, densities fulfill zero integral constraint. We propose a new bivariate spline basis for clr-transformed densities respecting the zero integral constraint. The practical impact of the proposed basis will be demonstrated on a regression model with a functional response for real geochemical data.
Czech name
—
Czech description
—

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
New Trends in Functional Statistics and Related Fields
ISBN
978-3-031-92382-1
ISSN
1431-1968
e-ISSN
2628-8966
Number of pages
8
Pages from-to
"503–510"
Publisher name
Springer
Place of publication
Cham
Event location
Novara, Itálie
Event date
Jun 25, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001545850800060

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