A Matrix-Variate Regression Model with Canonical States: An Application to Elderly Danish Twins

Laura Anderlucci (), Angela Montanari () and Cinzia Viroli ()
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Laura Anderlucci: Alma Mater Studiorum - Università di Bologna - Italy
Angela Montanari: Alma Mater Studiorum - Università di Bologna
Cinzia Viroli: Alma Mater Studiorum - Università di Bologna

Statistica, 2014, vol. 74, issue 4, 367-381

Abstract: In many situations we observe a set of variables in different states (e.g. times, replicates, locations) and the interest can be to regress the matrix-variate observed data on a set of covariates. We dene a novel matrix-variate regression model characterized by canonical components with the aim of analyzing the effect of covariates in describing the variability within and between the different states. Despite the seeming complexity, inference can be easily performed through maximum likelihood. We derive the inferential properties of the model estimators and a general approach for hypothesis testing. Finally, the proposed method is applied to data coming from the Longitudinal Study of Aging Danish Twins (LSADT), so to investigate the causes of variation in cognitive functioning.

Keywords: Linear Regression; Matrix-variate normal distribution; Maximum Likelihood; Structural equation modeling; Twin data (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:bot:rivsta:v:74:y:2014:i:4:p:367-381

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