Characterization of the multivariate Gauss-Markoff model with singular covariance matrix and missing values

Oktaba, Wiktor

doi:10.1023/a:1023215001376

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Hypothesis Testing On the Mgm-ib Model1

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Cited by 3 publications

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“…It is sufficient to apply Oktaba (1990) Theorem 2.1 to the Wishart distribution of S H and S e , which has been proved by Oktaba & Kieloch (1993), and to note that in the MGM-IB model the matrix θ of m = t + b + 1 parameters (t treatments, b blocks and 1 mean) in an incomplete block design with p variates of order m × p is partitioned into four submatrices as in (4). Remark 4.2.…”

Section: Hypothesis Testing On the Mgm-ib Modelmentioning

confidence: 99%

The General Multivariate Gauss—Markov Model of the Incomplete Block Design

Oktaba¹

2003

Aus NZ J of Statistics

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Section: Hypothesis Testing On the Mgm-ib Modelmentioning

confidence: 99%

The General Multivariate Gauss—Markov Model of the Incomplete Block Design

Oktaba¹

2003

Aus NZ J of Statistics

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On simultaneous prediction in a multivariate general linear model with future observations

Tian

Cheng

2017

Statistics & Probability Letters

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On the General Gauss-Markov Model for Experiments in Block Designs

Caliński

2012

Biometrical Letters

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SummaryThe main estimation and hypothesis testing results related to the Gauss− Markov model, in its general form, are recalled and the application of these results to the analysis of experiments in block designs is considered. Special attention is given to the randomization-derived model for a general block design, and for a proper block design in particular. The question whether the randomization-derived model can be considered as a particular general Gauss−Markov model is discussed. It is indicated that the former, as a mixed model, is in fact an extension of the general Gauss−Markov model. Thus, the analysis based on the randomization-derived model requires a more extended methodical approach. The present paper has been inspired by one of the last papers of Professor Wiktor Oktaba.

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Characterization of the multivariate Gauss-Markoff model with singular covariance matrix and missing values

Cited by 3 publications

References 13 publications

The General Multivariate Gauss—Markov Model of the Incomplete Block Design

The General Multivariate Gauss—Markov Model of the Incomplete Block Design

On simultaneous prediction in a multivariate general linear model with future observations

On the General Gauss-Markov Model for Experiments in Block Designs

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