In this paper, we estimate the moments of the selected population under asymmetric scale invariant loss function. We investigate risk-unbiased, consistency and admissibility of the natural estimators for the moments of the selected population. Finally, the risk-bias's and risks of the natural ...
Despite being the standard loss function to train multi-class neural networks, the log-softmax has two potential limitations. First, it involves computations that scale linearly with the number of output classes, which can restrict the size of problems we are able to tackle with current hardware...
In this paper, we estimate themoments of the selected population under asymmetric scale invariant loss function. We investigate risk-unbiased, consistency and admissibility of the natural estimatorsfor the moments of the selected population. Finally, the risk-bias's and risks of the natural ...
scale invariant squared error loss functionSUBSETLet X-1 and X-2 be two independent random variables representing the populations II1 and II2, respectively, and suppose that the random variable Xi has a gamma distribution with shape parameter p, same for both the populations, and unknown scale ...
V. Zidek, Minimax estimation of a bounded scale parameter for scale-invariant squared-error loss, Statist. Decisions, 17 (1999), 1-30.C. van Eeden and J. Zidek, Minimax estimation of a bounded scale parameter for scale-invariant squared-error loss, Statist. Decisions 17 (1999), 1-30....
scale invariant squared error loss functionrandom variablesshape parameteradmissible estimatorsequivariant estimatorsLet X 1 and X 2 be two independent random variables representing the populations Π 1 and Π 2, respectively, and suppose that the random variable X i has a gamma distribution with shape...