Construction of experimental designs for estimating variance componentsCitation formats

Standard

Construction of experimental designs for estimating variance components. / Loeza-Serrano, S.; Donev, A. N.

In: Computational Statistics and Data Analysis, Vol. 71, 03.2014, p. 1168-1177.

Research output: Contribution to journalArticlepeer-review

Harvard

Loeza-Serrano, S & Donev, AN 2014, 'Construction of experimental designs for estimating variance components', Computational Statistics and Data Analysis, vol. 71, pp. 1168-1177. https://doi.org/10.1016/j.csda.2012.10.008

APA

Loeza-Serrano, S., & Donev, A. N. (2014). Construction of experimental designs for estimating variance components. Computational Statistics and Data Analysis, 71, 1168-1177. https://doi.org/10.1016/j.csda.2012.10.008

Vancouver

Loeza-Serrano S, Donev AN. Construction of experimental designs for estimating variance components. Computational Statistics and Data Analysis. 2014 Mar;71:1168-1177. https://doi.org/10.1016/j.csda.2012.10.008

Author

Loeza-Serrano, S. ; Donev, A. N. / Construction of experimental designs for estimating variance components. In: Computational Statistics and Data Analysis. 2014 ; Vol. 71. pp. 1168-1177.

Bibtex

@article{680769a9d6f14920b7ae9e706b3c743b,
title = "Construction of experimental designs for estimating variance components",
abstract = "Many computer algorithms have been developed to construct experimental designs that are D-optimum for the fixed parameters of a statistical model. However, the case when the interest is in the variance components has not received much attention. This problem has similarities with that of designing experiments aiming at D-optimality for the fixed parameters of nonlinear models as its solution depends on the values of the unknown parameters that need to be estimated. An algorithm that can be used to construct locally and pseudo-Bayesian A- and D-optimum designs for the variance components in a linear mixed effects model, or for variance ratios, when there is a three-stage crossed or nested variability structure is proposed. Suitable visualizations of the results in order to help the assessment of the robustness of the designs against possible inaccuracies of the assumptions about the true values of the variance components used in the selection of the designs are recommended. {\textcopyright} 2013 Elsevier Inc. All rights reserved.",
keywords = "A-optimality, Crossed variability structure, D-optimality, Local optimality, Nested variability structure, Pseudo-Bayesian optimality",
author = "S. Loeza-Serrano and Donev, {A. N.}",
year = "2014",
month = mar,
doi = "10.1016/j.csda.2012.10.008",
language = "English",
volume = "71",
pages = "1168--1177",
journal = "Computational Statistics and Data Analysis",
issn = "0167-9473",
publisher = "Elsevier BV",

}

RIS

TY - JOUR

T1 - Construction of experimental designs for estimating variance components

AU - Loeza-Serrano, S.

AU - Donev, A. N.

PY - 2014/3

Y1 - 2014/3

N2 - Many computer algorithms have been developed to construct experimental designs that are D-optimum for the fixed parameters of a statistical model. However, the case when the interest is in the variance components has not received much attention. This problem has similarities with that of designing experiments aiming at D-optimality for the fixed parameters of nonlinear models as its solution depends on the values of the unknown parameters that need to be estimated. An algorithm that can be used to construct locally and pseudo-Bayesian A- and D-optimum designs for the variance components in a linear mixed effects model, or for variance ratios, when there is a three-stage crossed or nested variability structure is proposed. Suitable visualizations of the results in order to help the assessment of the robustness of the designs against possible inaccuracies of the assumptions about the true values of the variance components used in the selection of the designs are recommended. © 2013 Elsevier Inc. All rights reserved.

AB - Many computer algorithms have been developed to construct experimental designs that are D-optimum for the fixed parameters of a statistical model. However, the case when the interest is in the variance components has not received much attention. This problem has similarities with that of designing experiments aiming at D-optimality for the fixed parameters of nonlinear models as its solution depends on the values of the unknown parameters that need to be estimated. An algorithm that can be used to construct locally and pseudo-Bayesian A- and D-optimum designs for the variance components in a linear mixed effects model, or for variance ratios, when there is a three-stage crossed or nested variability structure is proposed. Suitable visualizations of the results in order to help the assessment of the robustness of the designs against possible inaccuracies of the assumptions about the true values of the variance components used in the selection of the designs are recommended. © 2013 Elsevier Inc. All rights reserved.

KW - A-optimality

KW - Crossed variability structure

KW - D-optimality

KW - Local optimality

KW - Nested variability structure

KW - Pseudo-Bayesian optimality

U2 - 10.1016/j.csda.2012.10.008

DO - 10.1016/j.csda.2012.10.008

M3 - Article

VL - 71

SP - 1168

EP - 1177

JO - Computational Statistics and Data Analysis

JF - Computational Statistics and Data Analysis

SN - 0167-9473

ER -