A kernel spatial density estimation allowing for the analysis of spatial clustering. Application to Monsoon Asia Drought Atlas data

摘要: A nonparametric density estimate that incorporates spatial dependency has not been studied in the literature. In this article, we propose a new estimator depends on two kernels: one controls distance between observations while other dependence structure. The uniform almost sure convergence of is established with rate convergence. consistency mode kernel also studied. Then hierarchical unsupervised clustering algorithm based presented. Some simulations as well an application to Monsoon Asia Drought Atlas data illustrate efficiency our algorithm, and comparison structures these detected by are done.