Least squares and generalized least squares in models with orthogonal block structure

Miguel dos Santos Fonseca

doi:10.1016/j.jspi.2009.12.007

Least squares and generalized least squares in models with orthogonal block structure

Miguel dos Santos Fonseca

DM - Departamento de Matemática

Research output: Contribution to journal › Article › peer-review

8 Citations (Scopus)

Abstract

Besides the basic model, Kronecker products of rotated models are used to isolate the variance components as parameters of a linear model. A characterization of BLUE given by Zmyslony (1980) is applied to the different models. Generalized least squares are used to complete the estimation. (C) 2009 Elsevier B.V. All rights reserved.

Original language	Unknown
Pages (from-to)	1346-1352
Journal	Journal Of Statistical Planning And Inference
Volume	140
Issue number	5
DOIs	https://doi.org/10.1016/j.jspi.2009.12.007
Publication status	Published - 1 Jan 2010

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10.1016/j.jspi.2009.12.007

Cite this

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title = "Least squares and generalized least squares in models with orthogonal block structure",

abstract = "Besides the basic model, Kronecker products of rotated models are used to isolate the variance components as parameters of a linear model. A characterization of BLUE given by Zmyslony (1980) is applied to the different models. Generalized least squares are used to complete the estimation. (C) 2009 Elsevier B.V. All rights reserved.",

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AB - Besides the basic model, Kronecker products of rotated models are used to isolate the variance components as parameters of a linear model. A characterization of BLUE given by Zmyslony (1980) is applied to the different models. Generalized least squares are used to complete the estimation. (C) 2009 Elsevier B.V. All rights reserved.

KW - models

KW - components

KW - Orthogonal

KW - variance

KW - experiments

KW - Inference

KW - mixed

KW - block

KW - randomized

KW - Linear

KW - structure

KW - Variance

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DO - 10.1016/j.jspi.2009.12.007

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VL - 140

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JO - Journal Of Statistical Planning And Inference

JF - Journal Of Statistical Planning And Inference

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ER -