We study the positive-definite completion problem for kernels on a varie...
We consider the problem of recovering conditional independence relations...
We present an optimal transport framework for performing regression when...
We establish a strong law of large numbers and a central limit theorem i...
We consider the problem of defining and fitting models of autoregressive...
We develop a generalisation of Mercer's theorem to operator-valued kerne...
Let X = {X_u}_u ∈ U be a real-valued Gaussian process indexed by a
set U...
We consider the problem of comparing several samples of stochastic proce...
Distribution-on-distribution regression considers the problem of formula...
We consider the problem of estimating the autocorrelation operator of an...
We propose nonparametric estimators for the second-order central moments...
Is it possible to detect if the sample paths of a stochastic process alm...
We consider the problem of nonparametric estimation of the drift and
dif...
We consider the problem of positive-semidefinite continuation: extending...
Functional data are typically modeled as sampled paths of smooth stochas...
We present a framework for performing regression when both covariate and...
Covariance estimation is ubiquitous in functional data analysis. Yet, th...
The non-parametric estimation of covariance lies at the heart of functio...
We develop methodology allowing to simulate a stationary functional time...
We consider the problem of covariance estimation for replicated space-ti...
A (lagged) time series regression model involves the regression of scala...
How can we discern whether a mean-square continuous stochastic process i...
Functional time series analysis, whether based on time of frequency doma...
Wasserstein distances are metrics on probability distributions inspired ...
Covariance operators are fundamental in functional data analysis, provid...
Contamination of covariates by measurement error is a classical problem ...