Statistical analysis of the moving least-squares method with unbounded sampling
Moving least-squares method is investigated with samples drawn from unbounded sampling processes. Convergence analysis is established by imposing incremental conditions on moments of sample output and window width. Satisfied convergence rates are derived by means of projection operator and some concentration inequalities.
moving least-squares method unbounded sampling concentration inequality learning theory sample error