0462344 20240103212550.120160908235959.9 10.1002/9781118947074.ch11 480 28 s. P cav_un_epca*04629769781118947043Part III: GRANGER CAUSALITY AND LONGITUDINAL DATA MODELING249-276HobokenJohn Wiley & Sons2016 causality life sciences cav_un_auth*0247122 Adaptivní systémy Department of Adaptive Systems AS AS Hlaváčková-Schindler Kateřina UTIA-B CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0333744 Naumova V. NO cav_un_auth*0333745 Pereverzyev S. AT http://library.utia.cas.cz/separaty/2016/AS/hlavackova-schindler-0462344.pdf cav_un_auth*0292725 GA13-13502S GA ČR 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. BD 2017 3 RVO:67985556 http://hdl.handle.net/11104/0262293 cav_un_auth*0298184 University of Innsbruck AT cav_un_auth*0333746 Simula Research Laboratory NO S 2016 cav_un_epca*0462976 Statistics and Causality: Methods for Applied Empirical Research Part III: GRANGER CAUSALITY AND LONGITUDINAL DATA MODELING 9781118947043 249 276 Hoboken John Wiley & Sons 2016 Wiley series in probability and statistics