363 citations · 772 across the 19 of their papers we have counts for
21 papers
Inference for sparsely sampled Gauss-Markov processes under outcome-dependent dropout
Alexander Aue, Siegfried Hörmann, Maximilian Ofner
We consider sparsely observed functional data generated by a latent Gauss-Markov process modeled through a linear stochastic differential equation (SDE). Motivated by an applicatio…
Quantifying and testing dependence to categorical variables
Siegfried Hörmann, Daniel Strenger-Galvis
We suggest a dependence coefficient between a categorical variable and some general variable taking values in a metric space. We derive important theoretical properties and study t…
Testing the Missing Completely at Random Assumption for Functional Data
Maximilian Ofner, Siegfried Hörmann, David Kraus +1
We consider functional data which have only been observed on a subset of their domain. This paper aims to develop statistical tests to determine whether the function and the domain…
Azadkia-Chatterjee's dependence coefficient for infinite dimensional data
Siegfried Hörmann, Daniel Strenger
We extend the scope of Azadkia-Chatterjee's dependence coefficient between a scalar response and a multivariate covariate to the case where takes values in a general me…
Covariate-informed reconstruction of partially observed functional data via factor models
Maximilian Ofner, Siegfried Hörmann
This paper studies linear reconstruction of partially observed functional data which are recorded on a discrete grid. We propose a novel estimation approach based on approximate fa…
Prediction in functional regression with discretely observed and noisy covariates
Siegfried Hörmann, Fatima Jammoul
In practice functional data are sampled on a discrete set of observation points and often susceptible to noise. We consider in this paper the setting where such data are used as ex…