284 citations · 806 across the 87 of their papers we have counts for
7 papers · 2 filters
Global Depths for Irregularly Observed Multivariate Functional Data
Zhuo Qu, Wenlin Dai, Marc G. Genton
Two frameworks for multivariate functional depth based on multivariate depths are introduced in this paper. The first framework is multivariate functional integrated depth, and the…
Nonseparable Space-Time Stationary Covariance Functions on Networks cross Time
Emilio Porcu, Philip A. White, Marc G. Genton
The advent of data science has provided an increasing number of challenges with high data complexity. This paper addresses the challenge of space-time data where the spatial domain…
Multivariate Functional Outlier Detection using the FastMUOD Indices
Oluwasegun Taiwo Ojo, Antonio Fernández Anta, Marc G. Genton +1
We present definitions and properties of the fast massive unsupervised outlier detection (FastMUOD) indices, used for outlier detection (OD) in functional data. FastMUOD detects ou…
Large-Scale Low-Rank Gaussian Process Prediction with Support Points
Yan Song, Wenlin Dai, Marc G. Genton
Low-rank approximation is a popular strategy to tackle the "big n problem" associated with large-scale Gaussian process regressions. Basis functions for developing low-rank structu…
Spatio-Temporal Cross-Covariance Functions under the Lagrangian Framework with Multiple Advections
Mary Lai O. Salvaña, Amanda Lenzi, Marc G. Genton
When analyzing the spatio-temporal dependence in most environmental and earth sciences variables such as pollutant concentrations at different levels of the atmosphere, a special p…
Robust Two-Layer Partition Clustering of Sparse Multivariate Functional Data
Zhuo Qu, Wenlin Dai, Marc G. Genton
A novel elastic time distance for sparse multivariate functional data is proposed and used to develop a robust distance-based two-layer partition clustering method. With this propo…
Scalable Gaussian-process regression and variable selection using Vecchia approximations
Jian Cao, Joseph Guinness, Marc G. Genton +1
Gaussian process (GP) regression is a flexible, nonparametric approach to regression that naturally quantifies uncertainty. In many applications, the number of responses and covari…