Titlebook: Bilinear Regression Analysis; An Introduction Dietrich von Rosen Book 2018 Springer International Publishing AG, part of Springer Nature 20

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https://doi.org/10.1007/978-981-13-3699-7 approach is extended to cover tensor space decompositions which is a basic tool when considering bilinear regression models. The decompositions are illustrated in figures where one can follow how maximum likelihood estimators are obtained by projecting on appropriate subspaces.

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Energy Security and Territorial Disputesrived for all estimators as well as the covariance among the estimators from the same model. Calculations use knowledge about the matrix normal, Wishart and inverted Wishart distributions. It is shown that the estimators are asymptotically equivalent to normally distributed random variables.

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Energy Security and Territorial Disputeson model the approximating density also appears to be a density. It can be shown that under some conditions the density represents a mixture of a normal distribution and a matrix Kotz-distribution. Similar results are shown to be available for the extended bilinear regression models.

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The Basic Ideas of Obtaining MLEs: Unknown Dispersion,sitions of the tensor space where within-individuals spaces also have an inner product which has to be estimated. All obtained estimators have explicit forms. A short literature review of bilinear regression models is given.

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Basic Properties of Estimators,rived for all estimators as well as the covariance among the estimators from the same model. Calculations use knowledge about the matrix normal, Wishart and inverted Wishart distributions. It is shown that the estimators are asymptotically equivalent to normally distributed random variables.