Regret
Leonard J. Savage argued that using non-Bayesian methods such as minimax, the loss function should be based on the idea of regret, i.e., the loss associated with a decision should be … See more

In some contexts, the value of the loss function itself is a random quantity because it depends on the outcome of a random variable X.
Statistics See more

Sound statistical practice requires selecting an estimator consistent with the actual acceptable variation experienced in the context of a particular applied problem. Thus, in the applied … See more

• Aretz, Kevin; Bartram, Söhnke M.; Pope, Peter F. (April–June 2011). "Asymmetric Loss Functions and the Rationality of Expected Stock … See more

In many applications, objective functions, including loss functions as a particular case, are determined by the problem formulation. In other situations, the decision maker’s preference must be elicited and represented by a scalar-valued function … See more

A decision rule makes a choice using an optimality criterion. Some commonly used criteria are:
• Minimax: Choose the decision rule with the lowest worst loss — that is, minimize the worst-case (maximum possible) loss:
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• Bayesian regret
• Loss functions for classification
• Discounted maximum loss
• Hinge loss
• Scoring rule See more