loss function


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loss function

[′lȯs ‚fəŋk·shən]
(mathematics)
In decision theory, the function, dependent upon the decision and the true underlying distributions, which expresses the loss produced in taking the decision.
References in periodicals archive ?
Otherwise, QLIKE is an asymmetric loss function that penalizes under-prediction more heavily than over-prediction, it is more suitable for applications such as risk management and VaR forecasting, where under-prediction of volatility can be more costly than over-prediction (Sharma & Vipul, 2016).
Keywords: financial stability; financial crises; macroprudential policy; loss function
In the case of a singular loss function, the Bayesian estimation of the hidden component can be found as a maximum a posteriori probability (MAP) estimation, which leads us to the problem of minimization of the objective function, often called the Gibbs energy function [2].
Kiley and Roberts derive optimal inflation targets in their two models, using three versions of a loss function that depends on inflation and the output gap.
However, optimizing of tree regularized classifier is challenging, since both of tree regularizer and hinge loss function in the proposed tree regularized classifier are non-differentiable.
In Bayesian statistical inference, the loss function plays an important role, and symmetric loss function, such as the squared error loss L([?
The expected value of the loss function is called the risk functional R(w):
The other integral part of Bayesian inference is the choice of loss function.
S)], the minimization empirical risk over loss function l([degrees]) with parameter vector d can be expressed as follows:
Mathematically, it is equivalent to the assumption that both the ceded-loss functions and the retained loss function are increasing.
The theoretical basis of linear Taylor rule rests on two key assumptions, namely that central banks have quadratic loss function and that the Phillips curve is linear.
t,m,h]) denotes the value of a given loss function of the forecast error at time t of country j for maturity m = 3-month, 10-year, and forecast horizons (in months) h = 3-month, 12-month.