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Bibliografie

Monography Chapter

Granger causality for ill-posed problems: Ideas, methods, and application in life sciences

Hlaváčková-Schindler Kateřina, Naumova V., Pereverzyev S.

: Statistics and Causality: Methods for Applied Empirical Research, p. 249-276

: GA13-13502S, GA ČR

: causality, life sciences

: 10.1002/9781118947074.ch11

: http://library.utia.cas.cz/separaty/2016/AS/hlavackova-schindler-0462344.pdf

(eng): 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

07.01.2019 - 08:39