Statistical estimation

Point estimation theory: bias, risk and mean squared error, likelihood and maximum likelihood (consistency and asymptotic normality), Fisher information and Cramér–Rao, sufficiency, factorization (Fisher–Neyman, Halmos–Savage) and Rao–Blackwell, Stein's lemma and James–Stein shrinkage, and contiguity and local asymptotic normality.

• Bias and mean squared error of estimators
• Contiguity and Le Cam's lemmas
• Fisher information and the Cramér–Rao bound
• Asymptotic normality of maximum likelihood
• Likelihood and maximum likelihood estimators
• Stein's lemma and James–Stein estimation
• Sufficient statistics and the factorization theorem
• Rao–Blackwell theorem

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