Introduced more than a half-century ago, Granger causality has become a popular tool for
analyzing time series data in many application domains, from economics and finance to …

Graphs representing complex systems often share a partial underlying structure across
domains while retaining individual features. Thus, identifying common structures can shed …

Learning the latent network structure from large scale multivariate point process data is an
important task in a wide range of scientific and business applications. For instance, we might …

We consider the problem of estimating the difference between two undirected functional
graphical models with shared structures. In many applications, data are naturally regarded …

This article presents a novel approach for reconstructing an equivalent underlying model
and deriving a precise equivalent expression through the use of direct causality topology …

Quantile regression has been successfully used to study heterogeneous and heavy-tailed
data. Varying-coefficient models are frequently used to capture changes in the effect of input …

The Hawkes process models have been recently become a popular tool for modeling and
analysis of neural spike train data. Despite this popularity, existing methodological and …

Thanks to technological advances leading to near-continuous time observations, emerging
multivariate point process data offer new opportunities for causal discovery. However, a key …

The Hawkes process models have been recently become a popular tool for modeling and
analysis of neural spike trains. In this article, motivated by neuronal spike trains study, we …

This dissertation addresses the challenges associated with correlated data in diverse fields,
emphasizing both statistical methodology and applications in the realms of genetics and …