In statistical decision theory, a minimax estimator δ M {\displaystyle \delta ^{M}\,\! } is an estimator which performs best in the worst possible case allowed in a problem. With problems of estimating a deterministic parameter (vector) θ ∈ Θ {\displaystyle \theta \in \Theta } from observations x ∈ X , {\displaystyle x\in {\mathcal {X}},} an estimator (estimation rule) δ M {\displaystyle \delta ^{M}\,\!
In statistical decision theory, a minimax estimator δ M {\displaystyle \delta ^{M}\,\! } is an estimator which performs best in the worst possible case allowed in a problem. With problems of estimating a deterministic parameter (vector) θ ∈ Θ {\displaystyle \theta \in \Theta } from observations x ∈ X , {\displaystyle x\in {\mathcal {X}},} an estimator (estimation rule) δ M {\displaystyle \delta ^{M}\,\!
Source: Wikipedia "Minimax estimator" · CC BY-SA 4.0
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