Detecting and Correcting Parameter Errors in Power Grids

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Detecting and Correcting Parameter Errors in Power Grids

Covers the detection and correction of parameter errors in power grids, focusing on statistical properties, error identification, computational efficiency, sensitivity analysis, and robust state estimation.

Point Estimation in Statistics

Explores point estimation in statistics, discussing bias, variance, mean squared error, and consistency of estimators.

Basic Principles of Point Estimation

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

Basics of Linear Regression

Covers the basics of linear regression, including OLS estimators, hypothesis testing, and confidence intervals.

Statistical Inference: Linear Models

Explores statistical inference for linear models, covering model fitting, parameter estimation, and variance decomposition.

Composite Materials: Bounds and Microstructures

Delves into homogenization in composite materials, deriving rigorous bounds and discussing statistical parameters and microstructures.

Statistical Estimation

Explores statistical estimation, comparing estimators based on mean and variance, and delving into mean squared error and Cramér-Rao bound.

Generalized Linear Models: A Brief Review

Provides an overview of Generalized Linear Models, focusing on logistic and Poisson regression models, and their implementation in R.

Linear Regression: Estimation and Inference

Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.

Maximum Likelihood Estimation

Introduces maximum likelihood estimation for statistical parameter estimation, covering bias, variance, and mean squared error.