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• W2055819614 abstract "Mixed effects models provide a suitable framework for statistical inference in a wide range of applications. The validity of likelihood inference for this class of models usually depends on the assumptions on random effects. We develop diagnostic tools for detecting random-effects model misspecification in a rich class of mixed effects models. These methods are illustrated via simulation and application to soybean growth data." @default.
• W2055819614 created "2016-06-24" @default.
• W2055819614 creator A5001818466 @default.
• W2055819614 date "2011-01-01" @default.
• W2055819614 modified "2023-09-27" @default.
• W2055819614 title "Detecting random-effects model misspecification via coarsened data" @default.
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• W2055819614 doi "https://doi.org/10.1016/j.csda.2010.06.012" @default.
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