Granger causality for ill-posed problems: Ideas, methods, and application in life sciences
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F16%3A00462344" target="_blank" >RIV/67985556:_____/16:00462344 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1002/9781118947074.ch11" target="_blank" >http://dx.doi.org/10.1002/9781118947074.ch11</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1002/9781118947074.ch11" target="_blank" >10.1002/9781118947074.ch11</a>
Alternative languages
Result language
angličtina
Original language name
Granger causality for ill-posed problems: Ideas, methods, and application in life sciences
Original language description
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.
Czech name
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Czech description
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Classification
Type
C - Chapter in a specialist book
CEP classification
BD - Information theory
OECD FORD branch
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Result continuities
Project
<a href="/en/project/GA13-13502S" target="_blank" >GA13-13502S: Fully Probabilistic Design of Dynamic Decision Strategies for Imperfect Participants in Market Scenarios</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Book/collection name
Statistics and Causality: Methods for Applied Empirical Research
ISBN
9781118947043
Number of pages of the result
28
Pages from-to
249-276
Number of pages of the book
480
Publisher name
John Wiley & Sons
Place of publication
Hoboken
UT code for WoS chapter
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