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Lecture
Frequency Moments: Estimators and Algorithms
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Related lectures (32)
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Estimators and Confidence Intervals
Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.
Estimators and Bias
Explores estimators, bias, and efficiency in statistics, emphasizing the trade-off between bias and variability.
Estimators: Consistency and Efficiency
Explores the criteria for good estimators, emphasizing consistency and efficiency in estimation.
Optimality in Decision Theory: Unbiased Estimation
Explores optimality in decision theory and unbiased estimation, emphasizing sufficiency, completeness, and lower bounds for risk.
Stochastic Simulations: Ergodicity and Estimators
Explores geometric ergodicity in Markov chains and estimators' bias and variance, highlighting efficiency loss quantification.
Parameter Estimation & Fisher Information
Covers parameter estimation, Fisher information, unbiased estimator, and exponential distributions.
Parameter Estimation: Detection & Estimation
Covers the concepts of parameter estimation, including unbiased estimators and Fisher information.
Risk and Return Measures
Covers risk and return measures, unbiasedness, and consistency of estimators.
The Stein Phenomenon and Superefficiency
Explores the Stein Phenomenon, showcasing the benefits of bias in high-dimensional statistics and the superiority of the James-Stein Estimator over the Maximum Likelihood Estimator.
Law of Large Numbers: Strong Convergence
Explores the strong convergence of random variables and the normal distribution approximation in probability and statistics.
