Convergence of $L_p$-norms of a matrix

Pavel Stavinoha

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In the paper four types of estimations of the linear function of the variance components are presented for the mixed linear model $𝐘 = 𝐗 β + 𝐞$ with expectation $E (𝐘) = 𝐗 β$ and covariance matrix $D (𝐘) = 0_{1} 𝐕_{1} + . . . + 0_{𝐦} 𝐕_{𝐦}$ .

A recursion formula for expected negative and positive powers of the central Wishart distribution.

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We use Haff's fundamental identity to express the expectation of S in lower-order terms, where S follows the central Wishart distribution.

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Displaying similar documents to “Convergence of L p -norms of a matrix”

Estimation of covariance components in a repeated regression experiment

Quadratic estimations in mixed linear models

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Repeated regression experiment and estimation of variance components

Displaying similar documents to “Convergence of $L_{p}$ -norms of a matrix”