Geometric Ergodicity: Convergence Diagnostics

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Covers the concept of point estimation in statistics, focusing on methods to estimate unknown parameters from a given sample.

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Covers Bayes estimator, Simulated Annealing, and EM for parameter estimation.

Covers Markov Chain Monte Carlo examples and error estimation methods.

Explores shrinkage estimation of high-dimensional covariance matrices, comparing linear and nonlinear approaches for improved accuracy.

Covers the estimation of distributions using samples and probability models.

Covers criteria for estimating parameters, emphasizing the importance of consistency, bias, variance, and efficiency of estimators.

Delves into optimal estimation, bias role in finite samples, and the delicate tradeoff between bias and variance in statistical estimation.

Explores statistical models, parameter estimation, and sampling distributions in probability and statistics.