1 citations · 3 across the 9 of their papers we have counts for
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Variable Domain Multivariate Functional Principal Component Analysis
Pavel Hernández Amaro, María Durbán, M. Carmen Aguilera-Morillo +3
Multivariate functional principal component analysis (MFPCA) is a powerful dimension reduction technique for analyzing multiple functional variables simultaneously. However, existi…
On spatial point processes with composition-valued marks
Matthias Eckardt, Mari Myllymäki, Sonja Greven
Methods for marked spatial point processes with scalar marks have seen extensive development in recent years. While the impressive progress in data collection and storage capacitie…
Generalized Multivariate Functional Additive Mixed Models for Location, Scale, and Shape
Alexander Volkmann, Nikolaus Umlauf, Sonja Greven
We propose a flexible regression framework to model the conditional distribution of multilevel generalized multivariate functional data of potentially mixed type, e.g. binary and c…
Flexible joint models for multivariate longitudinal and time-to-event data using multivariate functional principal components
Alexander Volkmann, Nikolaus Umlauf, Sonja Greven
The joint modeling of multiple longitudinal biomarkers together with a time-to-event outcome is a challenging modeling task of continued scientific interest. In particular, the com…
Functional Data Analysis: An Introduction and Recent Developments
Jan Gertheiss, David Rügamer, Bernard X. W. Liew +1
Functional data analysis (FDA) is a statistical framework that allows for the analysis of curves, images, or functions on higher dimensional domains. The goals of FDA, such as desc…
Adaptive Step-Length Selection in Gradient Boosting for Generalized Additive Models for Location, Scale and Shape
Boyao Zhang, Tobias Hepp, Sonja Greven +1
Tuning of model-based boosting algorithms relies mainly on the number of iterations, while the step-length is fixed at a predefined value. For complex models with several predictor…