Granger causality for ill-posed problems: Ideas, methods, and application in life sciences
Result 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.
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The result's identifiers
Result code in IS VaVaI
Result on the web
DOI - Digital Object Identifier
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
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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Basic information
Result type
C - Chapter in a specialist book
CEP
BD - Information theory
Year of implementation
2016