4 papers
Does PCA Work for Rough Functional Data?
Tim Kutta, Nina Dörnemann, Piotr Kokoszka
Functional data analysis is concerned with the analysis of infinite-dimensional data functions. Functional principal component analysis (FPCA) is a key method to obtain finite-dime…
Iterative Data-Consistent Inversion with Multiple Push-forward Constraints
Tianyi Jiang, Troy Butler, Timothy Wildey +2
A foundational challenge in uncertainty quantification involves estimating a probability measure on the space of uncertain parameters such that its push-forward through a computati…
Monitoring for a Phase Transition in a Time Series of Wigner Matrices
Nina Dörnemann, Piotr Kokoszka, Tim Kutta +1
We develop methodology and theory for the detection of a phase transition in a time-series of high-dimensional random matrices. In the model we study, at each time point \( t = 1,2…
Prokhorov Metric Convergence of the Partial Sum Process for Reconstructed Functional Data
Tim Kutta, Piotr Kokoszka
Motivated by applications in functional data analysis, we study the partial sum process of sparsely observed, random functions. A key novelty of our analysis are bounds for the dis…