cloture
发表于 2025-3-30 10:39:50
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.
饮料
发表于 2025-3-30 16:08:23
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 ..
Merited
发表于 2025-3-30 17:08:46
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臭名昭著
发表于 2025-3-31 00:06:23
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Mercurial
发表于 2025-3-31 04:27:40
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investigate
发表于 2025-3-31 05:36:16
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hair-bulb
发表于 2025-3-31 09:36:02
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.
monochromatic
发表于 2025-3-31 16:45:14
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.
Neuropeptides
发表于 2025-3-31 19:42:21
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Generosity
发表于 2025-4-1 01:41:02
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.