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math.ST2026
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…
math.ST2023
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…