Statistics and Causality: Methods for Applied Empirical Research

0462344 - ÚTIA 2017 RIV US eng M - Monography Chapter
Hlaváčková-Schindler, Kateřina - Naumova, V. - Pereverzyev, S.
Granger causality for ill-posed problems: Ideas, methods, and application in life sciences.
Statistics and Causality: Methods for Applied Empirical Research. Hoboken: John Wiley & Sons, 2016, s. 249-276. Wiley series in probability and statistics. ISBN 9781118947043
R&D Projects: GA ČR GA13-13502S
Institutional support: RVO:67985556
Keywords : causality * life sciences
Subject RIV: BD - Theory of Information
http://library.utia.cas.cz/separaty/2016/AS/hlavackova-schindler-0462344.pdf

Granger causality, based on a vector autoregressive model, is one of the most popular methods for uncovering the temporal dependencies between time series. The application of Granger causality to detect inference among a large number of variables (such as genes) requires a variable selection procedure. To address the lack of informative data, so-called regularization procedures are applied. In this chapter, we review current literature on Granger causality with Lasso regularization techniques for ill-posed problems (i.e., problems with multiple solutions). We discuss regularization procedures for inverse and ill-posed problems and present our recent approaches. These approaches are evaluated in a case study on gene regulatory networks reconstruction.
Permanent Link: http://hdl.handle.net/11104/0262293