Asymptotic properties of the growth curve model with covariance components

Ivan Žežula

Displaying similar documents to “Asymptotic properties of the growth curve model with covariance components”

Variance components and an additional experiment

Lubomír Kubáček (2012)

Applications of Mathematics

Similarity:

Estimators of parameters of an investigated object can be considered after some time as insufficiently precise. Therefore, an additional measurement must be realized. A model of a measurement, taking into account both the original results and the new ones, has a litle more complicated covariance matrix, since the variance components occur in it. How to deal with them is the aim of the paper.

Estimation of variance components in mixed linear models

Júlia Volaufová, Viktor Witkovský (1992)

Applications of Mathematics

Similarity:

The MINQUE of the linear function $\int^{'} ϑ$ of the unknown variance-components parameter $ϑ$ in mixed linear model under linear restrictions of the type $𝐑 ϑ = c$ is defined and derived. As an illustration of this estimator the example of the one-way classification model with the restrictions $ϑ_{1} = k ϑ_{2}$ , where $k \geq 0$ , is given.

Seemingly unrelated regression models

Lubomír Kubáček (2013)

Applications of Mathematics

Similarity:

The cross-covariance matrix of observation vectors in two linear statistical models need not be zero matrix. In such a case the problem is to find explicit expressions for the best linear unbiased estimators of both model parameters and estimators of variance components in the simplest structure of the covariance matrix. Univariate and multivariate forms of linear models are dealt with.

Estimation of a quadratic function of the parameter of the mean in a linear model

Júlia Volaufová, Peter Volauf (1989)

Aplikace matematiky

Similarity:

The paper deals with an optimal estimation of the quadratic function $β^{'} 𝐃 β$ , where $β \in ℛ^{k}, 𝐃$ is a known $k \times k$ matrix, in the model $𝐘, 𝐗 β, σ^{2} 𝐈$ . The distribution of $𝐘$ is assumed to be symmetric and to have a finite fourth moment. An explicit form of the best unbiased estimator is given for a special case of the matrix $𝐗$ .