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Extremum estimator
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Related lectures (32)
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Robust Regression: Methods and Applications
Explores robust and resistant methods in linear models, emphasizing the importance of handling extreme observations and the implications of robustness in regression models.
Statistical Estimation: Models and Learning Machines
Covers statistical estimation models, ML estimators, learning machines, practical issues, and challenges in estimation.
Parametric Models: Regression Estimators and Optimization
Covers parametric models, regression estimators, and optimization in statistical modeling.
Estimators and Confidence Intervals
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Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.
Estimators and Bias
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Explores estimators, bias, and efficiency in statistics, emphasizing the trade-off between bias and variability.
Law of Large Numbers: Strong Convergence
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Explores the strong convergence of random variables and the normal distribution approximation in probability and statistics.
Estimation Methods: Point Estimate and Maximum Likelihood
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Discusses different estimators and how to choose between them, emphasizing calculation and properties.
Estimators: Consistency and Efficiency
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Explores the criteria for good estimators, emphasizing consistency and efficiency in estimation.
Stochastic Simulations: Ergodicity and Estimators
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Explores geometric ergodicity in Markov chains and estimators' bias and variance, highlighting efficiency loss quantification.
Point Estimation in Statistics
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Covers the concept of point estimation in statistics, focusing on methods to estimate unknown parameters from a given sample.
Nonparametric Estimation: Empirical Likelihood Approach
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Explores nonparametric estimation using the empirical likelihood approach and discusses the computation of probabilities and empirical estimation.
Local Extremums of Functions
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Explains local and absolute extremums of functions and the classification of critical points.
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