SNEER
发表于 2025-3-25 06:58:50
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cataract
发表于 2025-3-25 07:43:12
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1FAWN
发表于 2025-3-25 12:44:00
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.
易改变
发表于 2025-3-25 19:23:39
Issues Decisive for China’s Rise or Fallsitions 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.
Mindfulness
发表于 2025-3-25 22:09:18
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.
TERRA
发表于 2025-3-26 00:27:59
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Scintigraphy
发表于 2025-3-26 05:09:22
https://doi.org/10.1007/978-981-13-3699-7gression models several natural residuals appear. The residuals are obtained by applying space decompositions of the tensor product of the between-individual and within-individual spaces. Density approximations are performed for the residuals. To obtain the distribution of the large residuals a para
适宜
发表于 2025-3-26 11:28:01
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Angioplasty
发表于 2025-3-26 14:26:04
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放气
发表于 2025-3-26 20:31:02
https://doi.org/10.1007/978-981-13-3699-7A short introduction to bilinear regression analysis is presented. The statistical paradigm is introduced. Moreover, bilinear regression models are presented together with a number of examples. Some historical remarks on the material of the book are given.