MATH 460 Theory of Statistics
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Review of probability, decision problems and loss functions, methods of point estimation: maximum likelihood and moment method, unbiasedness, consistency, sufficiency, Neyman-Fisher factorization theorem, Complete statistics. Lehmann-Scheffétheorem, Rao-Blackwell theorem. Fisher information and its properties. Cramer-Rao lower bound, Efficiency of estimators. Confidence Intervals: Theory and Construction, Hypothesis testing and Neyman-Pearson Lemma, Uniformly Most Powerful (UMP) and Likelihood Ratio Tests, Bayesian hypothesis testing. Asymptotic Methods in Estimation and Testing.
Credit units: 3 ECTS Credit units: 5, Prerequisite:
MATH 260.
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