# Risk function

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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

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

## ExamplesEdit

• For a scalar parameter $\theta$, a decision function whose output $\hat\theta$ is an estimate of $\theta$, 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
$R(f,\hat f)=E \|f-\hat f\|^2.\,$

## ReferencesEdit

• Template:SpringerEOM
• Berger, James O. (1985). Statistical decision theory and Bayesian Analysis, 2nd, New York: Springer-Verlag.
• DeGroot, Morris [1970] (2004). Optimal Statistical Decisions, Wiley Classics Library.
• Robert, Christian (2007). The Bayesian Choice, 2nd, New York: Springer.