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:
where
- θ is a fixed but possibly unknown state of nature;
- X is a vector of observations stochastically drawn from a population;
- 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 is an estimate of θ, and a quadratic loss function
- the risk function becomes the mean squared error of the estimate,
- In density estimation, the unknown parameter is probability density itself. The loss function is typically chosen to be a norm in an appropriate function space. For example, for L2 norm,
- the risk function becomes the mean integrated squared error
References
- Nikulin, M. S. (2001), "Risk of a statistical procedure", in Hazewinkel, Michiel, Encyclopedia of Mathematics, Springer, ISBN 978-1-55608-010-4
- Berger, James O. (1985). Statistical decision theory and Bayesian Analysis (2nd ed.). New York: Springer-Verlag. ISBN 0-387-96098-8. MR 0804611.
- DeGroot, Morris (2004) [1970]. Optimal Statistical Decisions. Wiley Classics Library. ISBN 0-471-68029-X. MR 2288194.
- Robert, Christian P. (2007). The Bayesian Choice (2nd ed.). New York: Springer. doi:10.1007/0-387-71599-1. ISBN 0-387-95231-4. MR 1835885.
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