Avoiding Boundary Estimates in Hierarchical Linear Models through Weakly Informative Priors [electronic resource] / Yeojin Chung, Sophia Rabe-Hesketh and Andrew Gelman.
Hierarchical or multilevel linear models are widely used for longitudinal or cross-sectional data on students nested in classes and schools, and are particularly important for estimating treatment effects in cluster-randomized trials, multi-site trials, and meta-analyses. The models can allow for va...
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Language: | English |
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2012.
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245 | 1 | 0 | |a Avoiding Boundary Estimates in Hierarchical Linear Models through Weakly Informative Priors |h [electronic resource] / |c Yeojin Chung, Sophia Rabe-Hesketh and Andrew Gelman. |
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520 | |a Hierarchical or multilevel linear models are widely used for longitudinal or cross-sectional data on students nested in classes and schools, and are particularly important for estimating treatment effects in cluster-randomized trials, multi-site trials, and meta-analyses. The models can allow for variation in treatment effects, as well as examination of the reasons for treatment effect variation. In this paper the authors propose a method that pulls the group-level standard deviation estimate off the boundary while producing estimates that are consistent with the data. The idea is to specify a weakly informative prior distribution for the standard deviation and to maximize the resulting posterior distribution, a method that can also be viewed as penalized maximum likelihood estimation. | ||
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650 | 0 | 7 | |a Maximum Likelihood Statistics. |2 ericd. |
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700 | 1 | |a Rabe-Hesketh, S., |e author. | |
700 | 1 | |a Gelman, Andrew, |e author. | |
700 | 1 | |a Dorie, Vincent, |e author. | |
700 | 1 | |a Liu, Jinchen, |e author. | |
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