Displaying similar documents to “Joint estimation for normal orthogonal mixed models”

Linear model genealogical tree application to an odontology experiment

Ricardo Covas (2007)

Discussiones Mathematicae Probability and Statistics

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Commutative Jordan algebras play a central part in orthogonal models. We apply the concepts of genealogical tree of an Jordan algebra associated to a linear mixed model in an experiment conducted to study optimal choosing of dentist materials. Apart from the conclusions of the experiment itself, we show how to proceed in order to take advantage of the great possibilities that Jordan algebras and mixed linear models give to practitioners.

ANOVA using commutative Jordan algebras, an application

Paulo Canas Rodrigues, João Tiago Mexia (2006)

Discussiones Mathematicae Probability and Statistics

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Binary operations on commutative Jordan algebras are used to carry out the ANOVA of a two layer model. The treatments in the first layer nests those in the second layer, that being a sub-model for each treatment in the first layer. We present an application with data retried from agricultural experiments.

Inference for random effects in prime basis factorials using commutative Jordan algebras

Vera M. Jesus, Paulo Canas Rodrigues, João Tiago Mexia (2007)

Discussiones Mathematicae Probability and Statistics

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Commutative Jordan algebras are used to drive an highly tractable framework for balanced factorial designs with a prime number p of levels for their factors. Both fixed effects and random effects models are treated. Sufficient complete statistics are obtained and used to derive UMVUE for the relevant parameters. Confidence regions are obtained and it is shown how to use duality for hypothesis testing.

Canonic inference and commutative orthogonal block structure

Francisco P. Carvalho, João Tiago Mexia, M. Manuela Oliveira (2008)

Discussiones Mathematicae Probability and Statistics

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It is shown how to define the canonic formulation for orthogonal models associated to commutative Jordan algebras. This canonic formulation is then used to carry out inference. The case of models with commutative orthogonal block structures is stressed out.

Orthogonal models: Algebraic structure and explicit estimators for estimable vectors

Artur Pereira, Miguel Fonseca, João Tiago Mexia (2015)

Discussiones Mathematicae Probability and Statistics

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We study the algebraic structure of orthogonal models thus of mixed models whose variance covariance matrices are all positive semi definite, linear combinations of known pairwise orthogonal projection matrices, POOPM, and whose least square estimators, LSE, of estimable vectors are best linear unbiased estimator, BLUE, whatever the variance components, so they are uniformly BLUE, UBLUE. From the results of the algebraic structure we will get explicit expression for the LSE of these...

Complete and sufficient statistics and perfect families in orthogonal and error orthogonal normal models

Aníbal Areia, Francisco Carvalho, João T. Mexia (2015)

Open Mathematics

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We will discuss orthogonal models and error orthogonal models and their algebraic structure, using as background, commutative Jordan algebras. The role of perfect families of symmetric matrices will be emphasized, since they will play an important part in the construction of the estimators for the relevant parameters. Perfect families of symmetric matrices form a basis for the commutative Jordan algebra they generate. When normality is assumed, these perfect families of symmetric matrices...

On maximum likelihood estimation in mixed normal models with two variance components

Mariusz Grządziel (2014)

Discussiones Mathematicae Probability and Statistics

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In the paper we deal with the problem of parameter estimation in the linear normal mixed model with two variance components. We present solutions to the problem of finding the global maximizer of the likelihood function and to the problem of finding the global maximizer of the REML likelihood function in this model.

On some properties of ML and REML estimators in mixed normal models with two variance components

Stanisław Gnot, Andrzej Michalski, Agnieszka Urbańska-Motyka (2004)

Discussiones Mathematicae Probability and Statistics

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In the paper, the problem of estimation of variance components σ₁² and σ₂² by using the ML-method and REML-method in a normal mixed linear model 𝒩 {Y,E(Y) = Xβ, Cov(Y) = σ₁²V + σ₂²Iₙ} is considered. This paper deal with properties of estimators of variance components, particularly when an explicit form of these estimators is unknown. The conditions when the ML and REML estimators can be expressed in explicit forms are given, too. The simulation study for one-way classification unbalanced...

Robust m-estimator of parameters in variance components model

Roman Zmyślony, Stefan Zontek (2002)

Discussiones Mathematicae Probability and Statistics

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It is shown that a method of robust estimation in a two way crossed classification mixed model, recently proposed by Bednarski and Zontek (1996), can be extended to a more general case of variance components model with commutative a covariance matrices.