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Parameter estimates from analyses of univariate twin data usually do not reflect the uncertainty due to
the model selection phase of the data analysis. To address the effect of model selection uncertainty on
parameter estimates, we introduce frequentist model-averaged estimators for univariate twin data analysis
that use information-theoretic criteria to assign model weights. We conduct simulation studies to examine
the performance of model-averaged estimators of additive genetic variance, and for tests for additive genetic
variance based on model-averaged estimators. In simulation studies with small to moderate sample sizes,
model-averaged estimators of additive genetic variance typically have lower mean-squared error than either
i) estimators from individual twin models, or ii) estimators obtained from a decision procedure where the
best-fitting model from likelihood-ratio testing is used to estimate additive genetic variance. For each
sample size simulated, bootstrap tests based on model-averaged estimators have higher power to detect additive
genetic variance than currently-used tests in most cases.
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