The Matérn family of covariance functions is currently the most popularl...
Spatial processes observed in various fields, such as climate and
enviro...
In this note, we investigate the non-identifiability of the multivariate...
We employ a general Monte Carlo method to test composite hypotheses of
g...
Two frameworks for multivariate functional depth based on multivariate d...
The advent of data science has provided an increasing number of challeng...
Low-rank approximation is a popular strategy to tackle the "big n proble...
We present definitions and properties of the fast massive unsupervised
o...
When analyzing the spatio-temporal dependence in most environmental and ...
In this work, a novel elastic time distance for sparse multivariate
func...
Gaussian process (GP) regression is a flexible, nonparametric approach t...
The transition from non-renewable to renewable energies represents a glo...
Mardia's measures of multivariate skewness and kurtosis summarize the
re...
This paper introduces the sparse functional boxplot and the intensity sp...
Fast and accurate hourly forecasts of wind speed and power are crucial i...
Correlated binary response data with covariates are ubiquitous in
longit...
Many proposals have emerged as alternatives to the Heckman selection mod...
Facing increasing societal and economic pressure, many countries have
es...
Predictive models for binary data are fundamental in various fields, and...
The assumption of normality has underlain much of the development of
sta...
The prevalence of multivariate space-time data collected from monitoring...
Modeling and inferring spatial relationships and predicting missing valu...
We propose a new class of extreme-value copulas which are extreme-value
...
We present a preconditioned Monte Carlo method for computing high-dimens...
For high-dimensional small sample size data, Hotelling's T2 test is not
...
Here, we address the problem of trend estimation for functional time ser...
We propose a parsimonious spatiotemporal model for time series data on a...
As high-dimensional and high-frequency data are being collected on a lar...
Due to the well-known computational showstopper of the exact Maximum
Lik...
A generalization of the definition of records to functional data is prop...
Parallel computing in Gaussian process calculation becomes a necessity f...
Environmental processes resolved at a sufficiently small scale in space ...
With the development of data-monitoring techniques in various fields of
...
Recently a blind source separation model was suggested for multivariate
...
Functional data analysis can be seriously impaired by abnormal observati...
Maximum likelihood estimation is an important statistical technique for
...
Maximum likelihood estimation is an important statistical technique for
...
Capturing the potentially strong dependence among the peak concentration...
Large, non-Gaussian spatial datasets pose a considerable modeling challe...
Quantifying the uncertainty of wind energy potential from climate models...
We use available measurements to estimate the unknown parameters (varian...
The classification of multivariate functional data is an important task ...