Given a sample from a finite population partitioned into classes, we consider estimating the distribution of the class frequencies. We propose first to estimate certain moments of this distribution, assuming Poisson sampling with unequal inclusion probabilities, and then to adapt these estimates...

For a set of spatially dependent dynamical models, we propose a method for estimating parameters that control temporal dynamics by spatial smoothing. The new approach is particularly relevant for analyzing spatially distributed panels of short time series. The asymptotic results show that...

We propose a bootstrap detection for operationally deterministic versus stochastic nonlinear modelling and illustrate the method with both simulated and real data sets.

We introduce a kernel-based estimator of the density function and regression function for data that have been grouped into family totals. We allow for a common intra-family component but require that observations from different families be in dependent. We establish consistency and asymptotic...