Risk function

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This article is about the mathematical definition of risk in statistical decision theory. For a more general discussion of concepts and definitions of risk, see the main article Risk.

In decision theory and estimation theory, the risk function R of a decision rule δ, is the expected value of a loss function L:

R(\theta ,\delta )={{\mathbb  E}}_{\theta }L{\big (}\theta ,\delta (X){\big )}=\int _{{\mathcal  {X}}}L{\big (}\theta ,\delta (X){\big )}\,dP_{\theta }(X)

where

  • θ is a fixed but possibly unknown state of nature;
  • X is a vector of observations stochastically drawn from a population;
  • {{\mathbb  E}}_{\theta } is the expectation over all population values of X;
  • dPθ is a probability measure over the event space of X, parametrized by θ; and
  • the integral is evaluated over the entire support of X.

Examples

  • For a scalar parameter θ, a decision function whose output {\hat  \theta } is an estimate of θ, and a quadratic loss function
L(\theta ,{\hat  \theta })=(\theta -{\hat  \theta })^{2},
the risk function becomes the mean squared error of the estimate,
R(\theta ,{\hat  \theta })=E_{\theta }(\theta -{\hat  \theta })^{2}.
L(f,{\hat  f})=\|f-{\hat  f}\|_{2}^{2}\,,
the risk function becomes the mean integrated squared error
R(f,{\hat  f})=E\|f-{\hat  f}\|^{2}.\,

References


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