Dynamic spatial models are important tools for the study of complex systems like environmental systems. This paper presents an integrated model that has been designed to explore land use trajectories in a small region around Maroua, located in the far north of Cameroon. The model simulates competition between land use types taking into account a set of biophysical, socio-demographic and geo-economics driving factors. The model includes three modules. The dynamic simulation module combines results of the spatial analysis and prediction modules. Simulation results for each scenario can help to identify where changes occur. The model developed constitutes an efficient knowledge support system for exploratory research and land use planning.
We consider the problem of variable selection via penalized likelihood using nonconvex penalty functions. To maximize the non-differentiable and nonconcave objective function, an algorithm based on local linear approximation and which adopts a naturally sparse representation was recently proposed. However, although it has promising theoretical properties, it inherits some drawbacks of Lasso in high dimensional setting. To overcome these drawbacks, we propose an algorithm (MLLQA) for maximizing the penalized likelihood for a large class of nonconvex penalty functions. The convergence property of MLLQA and oracle property of one-step MLLQA estimator are established. Some simulations and application to a real data set are also presented.
We derive and analyze an a posteriori error estimator for nonconforming finite element approximation for the quasi-Stokes problem, which is based on the solution of local problem on stars with low cost computation, this indicator is equivalent to the energy error norm up to data oscillation, neither saturation assumption nor comparison with residual estimator are made.