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In order to determine prediction filter order in simulations, we mainly relied on our knowledge of input process order, and started with this order.
The Gamma mixture models reflected the bimodality of the evidence distributions and therefore were useful to automatically determine prediction thresholds.
For class prediction, we performed a 10-fold CV analysis to iteratively optimize the list of genes and to determine prediction accuracies.
For such an exercise, pharmacokinetic and response (pharmacodynamic) variability could be included within Monte Carlo simulations to determine prediction intervals for E2 and BMD.
Two models were developed using sequential multiple regression to determine prediction of, social and emotional loneliness from those IVs having significant bivariate association with either DVs.
The idea of the prediction profile likelihood presented here is to determine prediction confidence without an explicit sampling strategy for the parameter space.
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Furthermore, sex was dropped as a predictive variable in the optimal AIC determined prediction model.
We assessed predictors of endoscopic findings using logistic regression models, and determined prediction rules that maximised sensitivity and positive predictive value (PPV).
We determined prediction accuracy, also assessing the impact of screw design and host material.
This paper describes a robust method for determining prediction intervals for models derived by non-linear regression.
In this article we demonstrate that, when evaluating a method for determining prediction intervals, interval size matters more than coverage because the latter can be fixed at a chosen confidence level with good reliability.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com