Statistical Models and Parameter Estimation

Related lectures (30)

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Explores linear regression estimation, hypothesis testing, and practical applications in statistics.

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Covers the estimation of distributions using samples and probability models.

Explores the Method of Moments, Bias-Variance tradeoff, Consistency, Plug-In Principle, and Likelihood Principle in point estimation.

Explores estimating parameters through confidence intervals in linear regression and statistics.

Covers the estimation of distributions using various methods such as minimum loss and expectation.

Explores estimators, bias, and efficiency in statistics, emphasizing the trade-off between bias and variability.

Covers estimation of parameters, MSE, Fisher information, and the Rao-Blackwell Theorem.

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

Covers the concepts of parameter estimation, including unbiased estimators and Fisher information.