These mathematical properties include Bayes estimators and posterior risks for the unknown component and proportion parameters using the non-informative and the informative priors under squared error loss function, precautionary loss function and DeGroot loss function.
We construct an asymptotically sharp estimator which converges, for sup norm error loss, with a spatially dependent normalisation which is sensitive to the variations in the local amount of data.
Similarities can be determined in one of four ways: (1) by the expert opinion of a soil classification specialist; (2) by the distance between classes in a numerical taxonomy assessment; (3) by distance within a taxonomic hierarchy; or (4) by an error loss function.
James and Stein [Estimation with quadratic loss, Proceedings of the Fourth Berkely Symposium vol. 1, 1961, pp. 361 379] proposed a shrinkage estimator (James Stein estimator) which improves the least squares estimator with respect to the mean squares error loss function.
These estimators dominate the ordinary least squares estimator under squared error loss.
The Bayesian estimation of the reliability parameter has been also discussed under the assumption of independent gamma prior, squared error loss and Linex error loss functions.
Forecast rationality under squared error loss implies various bounds on second moments of the data across forecast horizons.
Instead of minimizing the 0 1 loss in classification, regression techniques commonly minimize squared error loss.
The cross-entropy error loss function aims to maximize the average classification accuracy, i.e. WA recall.
Now the routing overhead model (5) contains three unknown parameters: expected forward degree, probability of no collision, and the probability of no channel error loss.
