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Statistics and Causality: Methods for Applied Empirical Research
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SYSNO ASEP 0462344 Document Type M - Monograph Chapter R&D Document Type Monograph Chapter Title Granger causality for ill-posed problems: Ideas, methods, and application in life sciences Author(s) Hlaváčková-Schindler, Kateřina (UTIA-B)
Naumova, V. (NO)
Pereverzyev, S. (AT)Number of authors 3 Source Title Statistics and Causality: Methods for Applied Empirical Research, Part III: GRANGER CAUSALITY AND LONGITUDINAL DATA MODELING. - Hoboken : John Wiley & Sons, 2016 - ISBN 9781118947043 Pages s. 249-276 Number of pages 28 s. Number of pages 480 Publication form Print - P Language eng - English Country US - United States Keywords causality ; life sciences Subject RIV BD - Theory of Information R&D Projects GA13-13502S GA ČR - Czech Science Foundation (CSF) Institutional support UTIA-B - RVO:67985556 DOI 10.1002/9781118947074.ch11 Annotation 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. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2017
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