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A note on marginal and conditional independence

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  • Loperfido, Nicola
Abstract
Some statistical models imply that two random vectors are marginally independent as well as being conditionally independent with respect to another random vector. When the joint distribution of the vectors is normal, canonical correlation analysis may lead to relevant simplifications of the dependence structure. A similar application can be found in elliptical models, where linear independence does not imply statistical independence. Implications for Bayes analysis of the general linear model are discussed.

Suggested Citation

  • Loperfido, Nicola, 2010. "A note on marginal and conditional independence," Statistics & Probability Letters, Elsevier, vol. 80(23-24), pages 1695-1699, December.
    • Handle: RePEc:eee:stapro:v:80:y:2010:i:23-24:p:1695-1699
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    References listed on IDEAS

    as
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    Full references (including those not matched with items on IDEAS)

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