From sparse to dense functional data and beyond

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Partial Separability and Functional Graphical Models for Multivariate Gaussian Processes (2019, result): “Further investigation into graphical models for functional data may yield interesting insights into regime divisions similar to those found by [27].”
Fréchet means and Procrustes analysis in Wasserstein space (2017, background): “cally L2[0;1]), and resulting challenges are linked to innite dimensionality (e.g. ill-posed studentisation, Munk et al. [57], and discrete measurements of continuum random objects, Zhang & Wang [78]). In the second case, the data are seen as random elements of a nite-dimensional Riemannian mani”
Functional Data Analysis (2016, background): “These issues were recently explored in a manuscript (Zhang & Wang 2014), employing a general weight function and providing a comprehensive analysis of the asymptotic properties on a uniﬁed platform for three types of asymptotics, L 2 and L ∞ (uniform) convergence as well as asymptotic normality of…”