The Neyman-Fisher Factorization Theorem states that a statistical model can be factored into two components, where one component depends only on the data and the other depends only on the parameters. This theorem is crucial in identifying sufficient statistics, which play a key role in estimating parameters and improving the efficiency of estimators, particularly in relation to deriving the Rao-Blackwell theorem.
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