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 …

Studies with temporal or spatial resolution are crucial to understand the molecular dynamics
and spatial dependencies underlying a biological process or system. With advances in high …

While most classical approaches to Granger causality detection assume linear dynamics,
many interactions in real-world applications, like neuroscience and genomics, are inherently …

Time series data are a collection of chronological observations which are generated by
several domains such as medical and financial fields. Over the years, different tasks such as …

Regularized estimation in sparse high-dimensional time series models Page 1 The Annals
of Statistics 2015, Vol. 43, No. 4, 1535–1567 DOI: 10.1214/15-AOS1315 © Institute of …

Connections among different assets, asset classes, portfolios, and the stocks of individual
institutions are critical in examining financial markets. Interest in financial markets implies …

A graphical model is a statistical model that is associated with a graph whose nodes
correspond to variables of interest. The edges of the graph reflect allowed conditional …

Temporal data, representing chronological observations of complex systems, has always
been a typical data structure that can be widely generated by many domains, such as …

It remains unclear whether causal, rather than merely correlational, relationships in
molecular networks can be inferred in complex biological settings. Here we describe the …

The vector autoregressive (VAR) model has been widely used for modeling temporal
dependence in a multivariate time series. For large (and even moderate) dimensions, the …