@inbook {Simula.simula.3070,
	title = {Granger Causality for Ill-Posed Problems: Ideas, Methods, and Application in Life Sciences},
	booktitle = {Statistics and Causality: Methods for Applied Empirical Research},
	year = {2015},
	pages = {1-41},
	publisher = {John Wiley \& Sons Limited Wiley},
	organization = {John Wiley \& Sons Limited Wiley},
	chapter = {1},
	address = {West Sussex, United Kingdom},
	abstract = {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 big number of variables (genes) requires a variable selection procedure. To fight with lack of informative data, the so called regularization procedures are applied. In this chapter, we review current literature applying Granger causality with Lasso regularization technique for ill-posed problems. We discuss regularization procedures for inverse and ill-posed problems and present our recent approaches that are evaluated in a case study of gene regulatory networks reconstruction.},
	author = {Naumova, Valeriya and Hlavackova-Schindler, Katerina and Sergei V {Pereverzyev}},
	editor = {Wiedermann, Wolfgang and von Eye, Alexander}
}
