Significance, relevance and explainability in the machine learning age : an econometrics and financial data science perspective
Year of publication: |
2021
|
---|---|
Authors: | Hoepner, Andreas G. F. ; McMillan, David G. ; Vivian, Andrew ; Wese Simen, Chardin |
Published in: |
The European journal of finance. - London [u.a.] : Taylor & Francis Group, ISSN 1466-4364, ZDB-ID 2001610-4. - Vol. 27.2021, 1/2, p. 1-7
|
Subject: | explainability | explainable artificial intelligence (xai) | neural networks | regressions | relevance | significance | Künstliche Intelligenz | Artificial intelligence | Neuronale Netze | Neural networks | Regressionsanalyse | Regression analysis | Ökonometrie | Econometrics |
-
Kosasih, Edward Elson, (2024)
-
Combination forecasts of tourism demand with machine learning models
Claveria, Oscar, (2016)
-
Application of machine learning in quantitative investment strategies on global stock markets
Grudniewicz, Jan, (2021)
- More ...
-
Financial Data Science : The Birth of a New Financial Research Paradigm Complementing Econometrics?
Brooks, Chris, (2020)
-
Financial data science : the birth of a new financial research paradigm complementing econometrics?
Brooks, Chris, (2019)
-
Brooks, Chris, (2019)
- More ...