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Titlebook: Recent Developments in Multivariate and Random Matrix Analysis; Festschrift in Honou Thomas Holgersson,Martin Singull Book 2020 Springer Na

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51#
發(fā)表于 2025-3-30 10:39:50 | 只看該作者
Spectral Analysis of Large Reflexive Generalized Inverse and Moore-Penrose Inverse Matrices,behaviour in case the population covariance matrix is not a multiple of identity. In this paper, we study the spectral properties of a reflexive generalized inverse and of the Moore-Penrose inverse of the sample covariance matrix. The obtained results are used to assess the difference in the asymptotic behaviour of their eigenvalues.
52#
發(fā)表于 2025-3-30 16:08:23 | 只看該作者
Properties of BLUEs and BLUPs in Full vs. Small Linear Models with New Observations, supplemented with the new unobservable random vector .., coming from ..?=?..?+ .., where the covariance matrix of .. is known as well as the cross-covariance matrix between .. and .. We focus on comparing the BLUEs of .. and ., and BLUPs of .. and .. under . and ..
53#
發(fā)表于 2025-3-30 17:08:46 | 只看該作者
54#
發(fā)表于 2025-3-31 00:06:23 | 只看該作者
55#
發(fā)表于 2025-3-31 04:27:40 | 只看該作者
56#
發(fā)表于 2025-3-31 05:36:16 | 只看該作者
57#
發(fā)表于 2025-3-31 09:36:02 | 只看該作者
Comments on Maximum Likelihood Estimation and Projections Under Multivariate Statistical Models,the space of structured matrices. The results will be illustrated by examples of structures suitable for multivariate models with general mean, growth curve models as well as doubly multivariate models.
58#
發(fā)表于 2025-3-31 16:45:14 | 只看該作者
Space Decomposition and Estimation in Multivariate Linear Models,nto tractable models that are easy to handle for estimation and testing. On the other hand, we give another space decomposition approach used for obtaining explicit estimators in multivariate linear models. Some examples on how this decomposition is performed for specific multivariate linear models are presented for both approaches.
59#
發(fā)表于 2025-3-31 19:42:21 | 只看該作者
60#
發(fā)表于 2025-4-1 01:41:02 | 只看該作者
Approximating Noncentral Chi-Squared to the Moments and Distribution of the Likelihood Ratio Statisbetter than earlier attempts to fit to scaled versions of asymptotic central chi-square distributions. The results enlighten the complex role of the dimension of the multivariate variable in relation to the sample size, for asymptotic likelihood ratio distribution results to hold.
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