  Heterogeneity of response patterns is important in estimating the size of a
closed population from multiple recapture data when capture patterns are
different over time and location. In this paper, we extend the non-parametric
one layer latent class model for multiple recapture data proposed by
Manrique-Vallier (2016) to a nested latent class model with the first layer
modeling individual heterogeneity and the second layer modeling location-time
differences. Location-time groups with similar recording patterns are in the
same top layer latent class and individuals within each top layer class are
dependent. The nested latent class model incorporates hierarchical
heterogeneity into the modeling to estimate population size from multi-list
recapture data. This approach leads to more accurate population size estimation
and reduced uncertainty. We apply the method to estimating casualties from the
Syrian conflict.
