This article studies the use of asymmetric loss functions for the optimal prediction of positive-valued spatial processes. We focus on the family of power-divergence loss functions with properties such as continuity, convexity, connections to well known divergence measures, and the ability to control the asymmetry and behaviour of the loss function via a power parameter. The properties of power-divergence loss functions, optimal power-divergence (OPD) spatial predictors, and related measures of uncertainty quantification are studied. In addition, we examine in general the notion of asymmetry in loss functions defined for positive-valued spatial processes and define an asymmetry measure, which we apply to the family of power-divergence loss functions and other common loss functions. The paper concludes with a simulation study comparing the optimal power-divergence predictor to predictors derived from other common loss functions. Finally, we illustrate OPD spatial prediction on a dataset
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Chakraborti A. Quantum Decision Theory and Complexity Modelling.2023 chilecomparte.cl - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from chilecomparte.cl Daily Mail and Mail on Sunday newspapers.
Distinguished University of Chicago economics professor and Nobel Laureate Lars Peter Hansen spoke about “Uncertainty” in economics to 150 seminar attendees at the Charles F. Knight Executive Education & Conference Center on Tuesday, Oct. 17. Hansen is an expert on “Uncertainty” in economic modeling, which includes how factors like risk, ambiguity, and inherent flaws […]