Sequential Downscaling Methods for Estimation from Aggregate Data

TL;DR: This paper develops a sequential downcasting method for spatially distributed crop production consistent with known national-level statistics and in accordance with geographical datasets and agronomic knowledge and proves convergence of the method to a generalized cross-entropy maximizing solution.

Abstract: Global change processes raise new estimation problems challenging the conventional statistical methods. These methods are based on the ability to obtain observations from unknown true probability distributions, whereas the new problems require recovering information from only partially observable or even unobservable variables. For instance, aggregate data exist at global and national level regarding agricultural production, occurrence of natural disasters, on incomes, etc. without providing any clue as to possibly alarming diversity of conditions at local level. “Downscaling” methods in this case should achieve plausible estimation of local implications emerging from global tendencies by using all available evidences.