Open AccessInterpretability of Composite Indicators Based on Principal Components
Author(s) -
Kris Boudt,
Marco d’Errico,
Hong Anh Luu,
Rebecca Pietrelli
Publication year - 2022
Publication title -
journal of probability and statistics
Language(s) - English
Resource type - Journals
eISSN - 1687-9538
pISSN - 1687-952X
DOI - 10.1155/2022/4155384
Subject(s) - principal component analysis , interpretability , robustness (evolution) , composite indicator , composite index , resilience (materials science) , computer science , principal (computer security) , risk analysis (engineering) , dimensionality reduction , mathematical optimization , mathematics , econometrics , reliability engineering , artificial intelligence , engineering , business , biochemistry , chemistry , physics , gene , operating system , thermodynamics
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