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Bilinear regression with random effects and reduced rank restrictions
Stockholm University, Faculty of Social Sciences, Department of Statistics.ORCID iD: 0000-0002-8610-0365
2019 (English)In: Japanese journal of statistics and data science, ISSN 2520-8756Article in journal (Refereed) Epub ahead of print
Abstract [en]

Bilinear models with three types of effects are considered: fixed effects, random effects and latent variable effects. In the literature, bilinear models with random effects and bilinear models with latent variables have been discussed but there are no results available when combining random effects and latent variables. It is shown, via appropriate vector space decompositions, how to remove the random effects so that a well-known model comprising only fixed effects and latent variables is obtained. The spaces are chosen so that the likelihood function can be factored in a convenient and interpretable way. To obtain explicit estimators, an important standardization constraint on the random effects is assumed to hold. A theorem is presented where a complete solution to the estimation problem is given.

Place, publisher, year, edition, pages
2019.
Keywords [en]
Fixed effects, Growth curve model, Likelihood-based estimates, Random effects, Rank restrictions
National Category
Probability Theory and Statistics
Research subject
Statistics; Mathematical Statistics
Identifiers
URN: urn:nbn:se:su:diva-170320DOI: 10.1007/s42081-019-00050-2OAI: oai:DiVA.org:su-170320DiVA, id: diva2:1330954
Funder
Swedish Research Council, 2017-03003Available from: 2019-06-26 Created: 2019-06-26 Last updated: 2019-07-16

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von Rosen, Tatjanavon Rosen, Dietrich
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