It is important that imputation models be correctly specified for analyses to yield unbiased estimates, and random forest may help avoid the bias that can occur with parametric MICE if the latter's imputation models are misspecified.
These definitions are nontrivial, as both dimensions – the product and the geographic market – need to be correctly specified.
The simulation studies presented have shown that these methods can be adequate when the imputation model is correctly specified, and are an improvement over parametric imputation when the imputation model is misspecified.
By integrating (7) with respect to X1, X4, and X5, it can be shown that this analysis model is correctly specified and the true is (θ0, θ2, θ3, θ23) = (− 3, 0.5, 0.5, 1).
In other words, even if the transition and the missing models are correctly specified, joint modeling can reduce bias in parameter estimates of the transition model, but may not do so in those of the missing model.
Because we simulate under assumptions 2 5 with parameter values estimated from the real data, we expect that if our model is correctly specified, the simulated data can capture patterns observed in the real data.
However, it is important to note that this main effect can only be accurately estimated if the genotype frequency distribution is correctly specified (see next paragraph).
