Abstract
A bilinear regression model with rank restrictions imposed on the mean-parameter matrix and the dispersion matrix is studied. Maximum likelihood-inspired estimates are derived. The approach generalizes classical reduced rank regression analysis and principal component analysis. It is illustrabed via a simulation study and a real example that even for small dimensions, the method works similarly to reduced rank regression analysis whereas the approach in this article also can be used when the dimension is large.
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This research has been supported by the Swedish Research Council (2017-03003).
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von Rosen, D. (2021). Bilinear Regression with Rank Restrictions on the Mean and Dispersion Matrix. In: Arnold, B.C., Balakrishnan, N., Coelho, C.A. (eds) Methodology and Applications of Statistics. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-030-83670-2_9
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