
Learning delay dynamics for multivariate stochastic processes, with application to the prediction of the growth rate of COVID19 cases in the United States
Delay differential equations form the underpinning of many complex dynam...
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Dynamic Network Regression
Network data are increasingly available in various research fields, moti...
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Distributional Representation of Longitudinal Data: Visualization, Regression and Prediction
We develop a representation of Gaussian distributed sparsely sampled lon...
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Single Index Fréchet Regression
Single index models provide an effective dimension reduction tool in reg...
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Conditional Wasserstein Barycenters and Interpolation/Extrapolation of Distributions
Increasingly complex data analysis tasks motivate the study of the depen...
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Latent Transport Models for Multivariate Functional Data
Multivariate functional data present theoretical and practical complicat...
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Autoregressive Optimal Transport Models
Series of distributions indexed by equally spaced time points are ubiqui...
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Modeling TimeVarying Random Objects and Dynamic Networks
Samples of dynamic or timevarying networks and other random object data...
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Additive Models for Symmetric PositiveDefinite Matrices, Riemannian Manifolds and Lie groups
In this paper an additive regression model for a symmetric positivedefi...
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Highdimensional MANOVA via Bootstrapping and its Application to Functional and Sparse Count Data
We propose a new approach to the problem of highdimensional multivariat...
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Uniform convergence of local Fréchet regression and time warping for metricspacevalued trajectories
For realvalued functional data, it is well known that failure to separa...
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Wasserstein Regression
The analysis of samples of random objects that do not lie in a vector sp...
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Point Process Regression
Point processes in time have a wide range of applications that include t...
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Fréchet Change Point Detection
We propose a method to infer the presence and location of changepoints ...
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Total Variation Regularized Fréchet Regression for MetricSpace Valued Data
NonEuclidean data that are indexed with a scalar predictor such as time...
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Wasserstein Covariance for Multiple Random Densities
A common feature of methods for analyzing samples of probability density...
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Modeling Longitudinal Data on Riemannian Manifolds
When considering functional principal component analysis for sparsely ob...
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CrossComponent Registration for Multivariate Functional Data with Application to Longitudinal Growth Curves
Multivariate functional data are becoming ubiquitous with the advance of...
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Rank Dynamics for Functional Data
We study the dynamic behavior of crosssectional ranks over time for fun...
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Wasserstein Gradients for the Temporal Evolution of Probability Distributions
Many studies have been conducted on flows of probability measures, often...
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Associating Growth in Infancy and Cognitive Performance in Early Childhood: A functional data analysis approach
Physical growth traits can be naturally represented by continuous functi...
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Fréchet Estimation of TimeVarying Covariance Matrices From Sparse Data, With Application to the Regional CoEvolution of Myelination in the Developing Brain
Assessing brain development for small infants is important for determini...
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HansGeorg Müller
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