Let's say a large population was sampled, and data to construct a model to predict Y were gathered at part of the sample units. To account for correlation among individuals from the same site, sampling unit was included as a random effect in a mixed effects model. There is interest to predict Y of every individual on all sampling units, thus part of them does not pertain to a level found in the model fitting dataset.
This issue was already discussed in some posts, but IMHO answers were somehow divergent. Some say one should not use a mixed model to predict for new data that do not pertain to a level found in the fitting dataset. Some say you can, by holding random effects to 0. Therefore, I am looking for a clear an "authoritative" solution for this issue.
I would also like to hear about the validity of following approaches:
Using random effects for predictions of individuals of known levels, and holding random effects to 0 for individuals of unknown levels;
Holding random effects to 0 for all predictions, including for those individuals of known levels.
These approaches would be used under the following situation: predictions for individuals on sampling units are aggregated to compute predictions of Y at the sampling unit level, and probability-based estimators (e.g., simple expansion estimators) are used to estimate the population mean and variance based on sampling unit-level predictions.
Probably, approach (2) would generate less sampling variability, and thus a narrower confidence interval for the estimated population mean. Here, I am not considering the effects of model prediction uncertainties on the population estimates.
Thank you very much!