Comparison of naïve,Kenward–Roger,and parametric bootstrap interval approaches to small-sample inference in linear mixed models |
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Authors: | Vincent S Staggs |
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Institution: | 1. Health Services &2. Outcomes Research, Children's Mercy Hospitals &3. Clinics, Kansas City, Missouri, USA |
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Abstract: | In mixed models the mean square error (MSE) of empirical best linear unbiased estimators generally cannot be written in closed form. Unlike traditional methods of inference, parametric bootstrapping does not require approximation of this MSE or the test statistic distribution. Data were simulated to compare coverage rates for intervals based on the naïve MSE approximation and the method of Kenward and Roger, and parametric bootstrap intervals (Efron's percentile, Hall's percentile, bootstrap-t). The Kenward–Roger method performed best and the bootstrap-t almost as well. Intervals were also compared for a small set of real data. Implications for minimum sample size are discussed. |
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Keywords: | Hierarchical model Mixed model Multilevel model Parametric bootstrap |
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