Compositional Scalar-on-Function Regression with Application to Sediment Particle Size Distributions

0542305 - ÚACH 2022 RIV DE eng J - Journal Article
Talská, R. - Hron, K. - Matys Grygar, Tomáš
Compositional Scalar-on-Function Regression with Application to Sediment Particle Size Distributions.
Mathematical Geosciences. Roč. 53, č. 7 (2021), s. 1667-1695. ISSN 1874-8961. E-ISSN 1874-8953
R&D Projects: GA ČR(CZ) GA19-01768S
Institutional support: RVO:61388980
Keywords : Bayes space * Regression analysis * Probability density functions * Compositional splines
OECD category: Environmental sciences (social aspects to be 5.7)
Impact factor: 2.508, year: 2021
Method of publishing: Open access with time embargo

The chemical composition of sediments is controlled predominantly by the sediment grain size, and thus evaluating their relationship is an important task in sedimentary geochemistry. The grain size is characterized by the respective particle size distribution, which can be expressed as a probability density function. Because of the relative character of densities, the Bayes space methodology was employed to build a regression model between a real response and a density function as a covariate, here the chemical composition and the particle size density. For practical computations, density functions were expressed in the standard L-2 space using the centred logratio transformation and spline approximation of the input discretized densities was utilized by respecting the induced zero-integral constraint. After a concise simulation study, supporting the relevance of the proposed regression model, the new methodology was applied to examine the relationship between sediment grain size and geochemical composition, with samples being obtained in the Czech Republic in the Skalka Reservoir and in the Oh.re River floodplain upstream of the reservoir, to reveal proper grain size proxies. The Al/Si and Zr/Rb logratios in the sediments that were studied showed grain-size control, which makes them suitable for this purpose.
Permanent Link: http://hdl.handle.net/11104/0322722