In this article, we propose a description length guided unified Granger causality analysis (uGCA) framework for sequential medical imaging. While existing efforts of GCA focused on causal relation design and statistical methods their improvement, our strategy adopts the minimum (MDL) principle in procedure where MDL offers model selection criteria deciding optimal sense length. Under framework,...

Introduced more than a half-century ago, Granger causality has become popular tool for analyzing time series data in many application domains, from economics and finance to genomics neuroscience. Despite this popularity, the validity of framework inferring causal relationships among remained topic continuous debate. Moreover, while original definition was general, limitations computational tool...

This paper investigates the existence of Granger causality and cointegrated relationships between tourism related Foreign Direct Investment (FDI) and tourism development in developing countries using panel VECM techniques from 1995 to 2008. The results confirm the existence of a co-integrated relationship between variables in the long run. In addition, there is a bilateral long-run causality be...

This study attempts to investigate the effect of oil returns and external debt on the government expenditure in Syria over the period 1970-2010. The Johansen cointegration test showed that oil returns and external debt have a positive and significant long run relationship with government expenditure. The Granger causality test indicates unidirectional short-run causality relationships running f...

BACKGROUND We present a novel and systematic approach to analyze temporal microarray data. The approach includes normalization, clustering and network analysis of genes. METHODOLOGY Genes are normalized using an error model based uniform normalization method aimed at identifying and estimating the sources of variations. The model minimizes the correlation among error terms across replicates. ...

The ability to identify directional interactions that occur among multiple neurons in the brain is crucial to an understanding of how groups of neurons cooperate in order to generate specific brain functions. However, an optimal method of assessing these interactions has not been established. Granger causality has proven to be an effective method for the analysis of the directional interactions...

Measures of the direction and strength of the interdependence among time series from multivariate systems are evaluated based on their statistical significance and discrimination ability. The best-known measures estimating direct causal effects, both linear and nonlinear, are considered, i.e., conditional Granger causality index (CGCI), partial Granger causality index (PGCI), partial directed c...

This paper introduces the concept of homogeneous non-causality in heterogeneous panels. This concept is used to examine a panel of data for evidence of a causal relationship between GDP and carbon emissions. The technique is compared to the standard test for homogeneous non-causality in homogeneous panels and heterogeneous non-causality in heterogeneous panels. In North America, Asia and Oceani...

The problem of estimating high-dimensional network models arises naturally in the analysis of many biological and socio-economic systems. In this work, we aim to learn a network structure from temporal panel data, employing the framework of Granger causal models under the assumptions of sparsity of its edges and inherent grouping structure among its nodes. To that end, we introduce a group lass...