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On Estimation in Some Reduced Rank Extended Growth Curve Models
Stockholm University, Faculty of Social Sciences, Department of Statistics.
Number of Authors: 22017 (English)In: Mathematical Methods of Statistics, ISSN 1066-5307, E-ISSN 1934-8045, Vol. 26, no 4, p. 299-310Article in journal (Refereed) Published
Abstract [en]

The general multivariate analysis of variance model has been extensively studied in the statistical literature and successfully applied in many different fields for analyzing longitudinal data. In this article, we consider the extension of this model having two sets of regressors constituting a growth curve portion and a multivariate analysis of variance portion, respectively. Nowadays, the data collected in empirical studies have relatively complex structures though often demanding a parsimonious modeling. This can be achieved for example through imposing rank constraints on the regression coefficient matrices. The reduced rank regression structure also provides a theoretical interpretation in terms of latent variables. We derive likelihood based estimators for the mean parameters and covariance matrix in this type of models. A numerical example is provided to illustrate the obtained results.

Place, publisher, year, edition, pages
2017. Vol. 26, no 4, p. 299-310
Keywords [en]
growth curve model, maximum likelihood estimator, multivariate analysis of variance, reduced rank model
National Category
Mathematics
Identifiers
URN: urn:nbn:se:su:diva-151003DOI: 10.3103/S1066530717040044ISI: 000418508700004OAI: oai:DiVA.org:su-151003DiVA, id: diva2:1172806
Available from: 2018-01-10 Created: 2018-01-10 Last updated: 2018-01-10Bibliographically approved

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