Accurately presenting model estimates with appropriate uncertainties is critical to the credibility and defensibility of anypiece of statistical analysis. When dealing with complex data that require hierarchical covariance structures, many of the standardapproaches for visualizing uncertainty are insufficient. One such case is data fit with log-linear autoregressive mixed effectsmodels. Data requiring such an approach have three exceptional characteristics.1. The data are sampled in “groups” that exhibit variation unexplained by other model factors.2. The data are sampled over time and exhibit autocorrelation.3. The data originate from a skewed distribution.These data are addressed using a log-linear autoregressive mixed model (LLARMM), which accounts for each of thesecharacteristics.
Suggested Citation
Freeman, Laura J, and Matthew R Avery. Lognormal Data with Random Effects. IDA Document NS D-8629. Alexandria, VA: Institute for Defense Analyses, 2017.