The Evidential regression network (ENet) estimates a continuous target and its predictive uncertainty without costly Bayesian model averaging. However, it is possible that the inaccurately predicted due to gradient shrinkage problem of original loss function ENet, negative log marginal likelihood (NLL) loss. In this paper, objective improve prediction accuracy ENet while maintaining efficient e...

BACKGROUND Understanding the quality loss implications of short staffing is essential in maintaining service quality on a limited budget. OBJECTIVES For elaborate financial control on staffing decisions, it is necessary to quantify the cost of the incidental quality loss that a given workload and staffing level entail. DESIGN We develop a quantitative methodology that uses a quality loss fu...

Many performance metrics have been introduced in the literature for the evaluation of classification performance, each of them with different origins and areas of application. These metrics include accuracy, unweighted accuracy, the area under the ROC curve or the ROC convex hull, the mean absolute error and the Brier score or mean squared error (with its decomposition into refinement and calib...

This paper evaluates the out-of-sample forecasting accuracy of eleven models for monthly volatility in fifteen stock markets. Volatility is defined as within-month standard deviation of continuously compounded daily returns on the stock market index of each country for the ten-year period 1988 to 1997. The first half of the sample is retained for the estimation of parameters while the second ha...

Many performance metrics have been introduced in the literature for the evaluation of classification performance, each of them with different origins and areas of application. These metrics include accuracy, macro-accuracy, area under the ROC curve or the ROC convex hull, the mean absolute error and the Brier score or mean squared error (with its decomposition into refinement and calibration). ...

This paper addresses selection of the loss function for regression problems with finite data. It is well-known (under standard regression formulation) that for a known noise density there exist an optimal loss function under an asymptotic setting (large number of samples), i.e. squared loss is optimal for Gaussian noise density. However, in real-life applications the noise density is unknown an...

• In this paper, we propose Bayes estimators of the parameter of the exponentiated gamma distribution and associated reliability function under General Entropy loss function for a censored sample. The proposed estimators have been compared with the corresponding Bayes estimators obtained under squared error loss function and maximum likelihood estimators through their simulated risks (average l...

Based on progressively Type-II censored samples, the maximum likelihood and Bayes estimators for the scale parameter, reliability and cumulative hazard functions are derived. The Bayes estimators are studied under symmetric (squared error) loss function and asymmetric (LINEX and general entropy) loss functions. Tow techniques are used for computing the Bayes estimates; standard Bayes and import...