Fisher information and Cramer–Rao bound
For a differentiable scalar iid model with score sθ=∂θlog fθ(X), Fisher information is I(θ)=Eθ[sθ²].
Key knowledge
For a differentiable scalar iid model with score sθ=∂θlog fθ(X), Fisher information is I(θ)=Eθ[sθ²]. If differentiation can pass through the required expectations and the estimator is unbiased, Varθ(T)≥1/[nI(θ)].
Conditions and scope
- Support does not depend on θ; the score has mean zero and 0<I(θ)<∞.
- The bound is not stated for biased estimators or nonregular models.
Sources · 1
References
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Evidence and review
Lecture 15 score/information definitions and Theorem 15.4 with its expectation-differentiation proof conditions.
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