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''decision procedure, statistical decision rule''

A rule according to which statistical decisions are made on the basis of observations obtained.

Let $X$ be a random variable that takes values in a [[sample space]] $\left({\mathfrak{X},\mathcal{B},\mathbf{P}_\theta}\right)$, $\theta \in \Theta$, and let $D = \{ d \}$ be the set of all possible decisions $d$ that can be taken relative to the parameter $\theta$ with respect to a realization of $X$. According to the accepted terminology in mathematical statistics and the theory of games, any $\mathcal{B}$-measurable transformation $\delta : \mathfrak{X} \rightarrow D$ of the space of realizations $\mathfrak{X}$ of $X$ into the set of possible decisions $D$ is called a decision function. For example, in the statistical estimation of the parameter $d$ any [[point estimator]] $\hat\theta = \hat\theta(x)$ is a decision function. A basic problem in statistics in obtaining statistical conclusions is the choice of a decision function $\delta(\cdot)$ that minimizes the risk
$$
R(\theta,\delta) = \mathbf{E}_\theta[L(\theta,\delta_X)]
$$
relative to the loss function $L(\cdot,\cdot)$ used.

The concept of a decision function is a basic concept in the theory of statistical decision functions as developed by A. Wald.

====Comments====
Cf. also [[Statistical decision theory]].

====References====
<table>
<TR><TD valign="top">[1]</TD> <TD valign="top"> N.N. Chentsov, "Statistical decision laws and optimal inference" , Amer. Math. Soc.  (1982)  (Translated from Russian)</TD></TR>
<TR><TD valign="top">[2]</TD> <TD valign="top"> A. Wald, "Statistical decision functions" , Wiley  (1950)</TD></TR>
<TR><TD valign="top">[a1]</TD> <TD valign="top"> J.O. Berger, "Statistical decision theory. Foundations, concepts and models" , Springer  (1980)</TD></TR>
</table>

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