| bibtype |
M -
Monography Chapter
|
| ARLID |
0462344 |
| utime |
20240103212550.1 |
| mtime |
20160908235959.9 |
| DOI |
10.1002/9781118947074.ch11 |
| title
(primary) (eng) |
Granger causality for ill-posed problems: Ideas, methods, and application in life sciences |
| specification |
| book_pages |
480 |
| page_count |
28 s. |
| media_type |
P |
|
| serial |
| ARLID |
cav_un_epca*0462976 |
| ISBN |
9781118947043 |
| title
|
Statistics and Causality: Methods for Applied Empirical Research |
| part_title |
Part III: GRANGER CAUSALITY AND LONGITUDINAL DATA MODELING |
| page_num |
249-276 |
| publisher |
| place |
Hoboken |
| name |
John Wiley & Sons |
| year |
2016 |
|
|
| keyword |
causality |
| keyword |
life sciences |
| author
(primary) |
| ARLID |
cav_un_auth*0247122 |
| full_dept (cz) |
Adaptivní systémy |
| full_dept (eng) |
Department of Adaptive Systems |
| department (cz) |
AS |
| department (eng) |
AS |
| name1 |
Hlaváčková-Schindler |
| name2 |
Kateřina |
| institution |
UTIA-B |
| country |
CZ |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0333744 |
| name1 |
Naumova |
| name2 |
V. |
| country |
NO |
|
| author
|
| ARLID |
cav_un_auth*0333745 |
| name1 |
Pereverzyev |
| name2 |
S. |
| country |
AT |
|
| source |
|
| cas_special |
| project |
| ARLID |
cav_un_auth*0292725 |
| project_id |
GA13-13502S |
| agency |
GA ČR |
|
| abstract
(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. |
| RIV |
BD |
| reportyear |
2017 |
| num_of_auth |
3 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0262293 |
| cooperation |
| ARLID |
cav_un_auth*0298184 |
| name |
University of Innsbruck |
| country |
AT |
|
| cooperation |
| ARLID |
cav_un_auth*0333746 |
| name |
Simula Research Laboratory |
| country |
NO |
|
| confidential |
S |
| arlyear |
2016 |
| mrcbU63 |
cav_un_epca*0462976 Statistics and Causality: Methods for Applied Empirical Research Part III: GRANGER CAUSALITY AND LONGITUDINAL DATA MODELING 9781118947043 249 276 Hoboken John Wiley & Sons 2016 Wiley series in probability and statistics |
|