Functional data clustering is to identify heterogeneous morphological patterns in the
continuous functions underlying the discrete measurements/observations. Application of …

Functional data analysis (FDA), which is a branch of statistics on modeling infinite
dimensional random vectors resided in functional spaces, has become a major research …

Covariance estimation is essential yet underdeveloped for analysing multivariate functional
data. We propose a fast covariance estimation method for multivariate sparse functional data …

Studies of Alzheimer's disease (AD) often collect multiple longitudinal clinical outcomes,
which are correlated and predictive of AD progression. It is of great scientific interest to …

We introduce a novel method for separating amplitude and phase variability in exponential
family functional data. Our method alternates between two steps: the first uses generalized …

Reliable prediction for crop yield is crucial for economic planning, food security monitoring,
and agricultural risk management. This study aims to develop a crop yield forecasting model …

In this article, a new method is proposed for clustering longitudinal curves. In the proposed
method, clusters of mean functions are identified through a weighted concave pairwise …

We propose methods for variable selection in the context of modeling the association
between a functional response and concurrently observed functional predictors. This data …

In this paper, we introduce a model for multivariate, spatio-temporal functional data.
Specifically, this work proposes a mixture model that is used to perform spatio-temporal …

We propose a novel method for estimating population-level and subject-specific effects of
covariates on the variability of functional data. We extend the functional principal …