Testing for Main Random Effects in Two-Way Random and Mixed Effects Models: Modifying the Statistic

A procedure for testing the significance of the main random effect is proposed under a model which does not require the traditional assumptions of symmetry, homoscedasticity, and normality for the error term and random effects. To accommodate this level of model generality, and also unbalanced desig...

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Main Authors: Trent Gaugler, Michael G. Akritas
Format: Article
Language:English
Published: Wiley 2013-01-01
Series:Journal of Probability and Statistics
Online Access:http://dx.doi.org/10.1155/2013/708540
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author Trent Gaugler
Michael G. Akritas
author_facet Trent Gaugler
Michael G. Akritas
author_sort Trent Gaugler
collection DOAJ
description A procedure for testing the significance of the main random effect is proposed under a model which does not require the traditional assumptions of symmetry, homoscedasticity, and normality for the error term and random effects. To accommodate this level of model generality, and also unbalanced designs, suitable adjustments to the F-test are made. The extensive simulations performed under the random effects model, and the unrestricted and restricted versions of the mixed effects model, indicate that the classical F procedure is extremely liberal under heteroscedasticity and unbalancedness. The proposed test procedure performs well in all settings and is comparable to the classical F-test when the classical assumptions are met. An analysis of a dataset from the Mussel Watch Project is presented.
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institution Kabale University
issn 1687-952X
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publishDate 2013-01-01
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series Journal of Probability and Statistics
spelling doaj-art-23a28a031eeb4d35b11df3bc8013aa5b2025-02-03T05:49:42ZengWileyJournal of Probability and Statistics1687-952X1687-95382013-01-01201310.1155/2013/708540708540Testing for Main Random Effects in Two-Way Random and Mixed Effects Models: Modifying the StatisticTrent Gaugler0Michael G. Akritas1Department of Statistics, Carnegie Mellon University, 5000 Forbes Avenue, 132B Baker Hall, Pittsburgh, PA 15213, USADepartment of Statistics, Penn State University, 325 Thomas Building, University Park, PA 16802, USAA procedure for testing the significance of the main random effect is proposed under a model which does not require the traditional assumptions of symmetry, homoscedasticity, and normality for the error term and random effects. To accommodate this level of model generality, and also unbalanced designs, suitable adjustments to the F-test are made. The extensive simulations performed under the random effects model, and the unrestricted and restricted versions of the mixed effects model, indicate that the classical F procedure is extremely liberal under heteroscedasticity and unbalancedness. The proposed test procedure performs well in all settings and is comparable to the classical F-test when the classical assumptions are met. An analysis of a dataset from the Mussel Watch Project is presented.http://dx.doi.org/10.1155/2013/708540
spellingShingle Trent Gaugler
Michael G. Akritas
Testing for Main Random Effects in Two-Way Random and Mixed Effects Models: Modifying the Statistic
Journal of Probability and Statistics
title Testing for Main Random Effects in Two-Way Random and Mixed Effects Models: Modifying the Statistic
title_full Testing for Main Random Effects in Two-Way Random and Mixed Effects Models: Modifying the Statistic
title_fullStr Testing for Main Random Effects in Two-Way Random and Mixed Effects Models: Modifying the Statistic
title_full_unstemmed Testing for Main Random Effects in Two-Way Random and Mixed Effects Models: Modifying the Statistic
title_short Testing for Main Random Effects in Two-Way Random and Mixed Effects Models: Modifying the Statistic
title_sort testing for main random effects in two way random and mixed effects models modifying the statistic
url http://dx.doi.org/10.1155/2013/708540
work_keys_str_mv AT trentgaugler testingformainrandomeffectsintwowayrandomandmixedeffectsmodelsmodifyingthestatistic
AT michaelgakritas testingformainrandomeffectsintwowayrandomandmixedeffectsmodelsmodifyingthestatistic