ArticleStat (International Statistical Institute)2021
A mutual information criterion with applications to canonical correlation analysis and graphical models.
Article in Stat (International Statistical Institute), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- A mutual information criterion with applications to canonical correlation analysis and graphical models.Stat (International Statistical Institute) · 2021Article
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Authors and funding
2 authors.
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Abstract
This paper derives a criterion for deciding conditional independence that is consistent with small-sample corrections of Akaike's information criterion but is easier to apply to such problems as selecting variables in canonical correlation analysis and selecting graphical models. The criterion reduces to mutual information when the assumed distribution equals the true distribution; hence, it is called mutual information criterion (MIC). Although small-sample Kullback-Leibler criteria for these selection problems have been proposed previously, some of which are not widely known, MIC is strikingly more direct to derive and apply.
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Registered trials
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