Granger Causality for Ill-Posed Problems: Ideas, Methods, and Application in Life Sciences.

Granger Causality for Ill-Posed Problems: Ideas, Methods, and Application in Life Sciences.

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 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.

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Authors
  • Schindlerova, Katerina
  • Naumova, Valeriya
  • Pereverzyev Jr., Sergiy
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Editors
  • Wiedermann, Wolfgang
  • Von Eye, Alexander
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Shortfacts
Category
Book Section/Chapter
Divisions
Data Mining
Subjects
Datenstrukturen
Kuenstliche Intelligenz
Title of Book
Statistics and Causality
ISSN/ISBN
978-1-118-94704-3
Page Range
pp. 248-276
Date
July 2016
Official URL
http://eu.wiley.com/WileyCDA/WileyTitle/productCd-...
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