Generalized least squares

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Related lectures (31)

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Linear Regression: Maximum Likelihood Approach

Covers linear regression topics including confidence intervals, variance, and maximum likelihood approach.

Weighted Least Squares Estimation: IRLS Algorithm

Explores the IRLS algorithm for weighted least squares estimation in GLM.

Instrumental Variables: Addressing Measurement Error and Reverse Causality

Explores how instrumental variables correct biases from measurement error and reverse causality in regression models.

Least Squares Approximation

Explains least squares approximation for finding best fit lines or curves to data points.

Basics of linear regression model

Covers the basics of linear regression, OLS method, predicted values, residuals, matrix notation, goodness-of-fit, hypothesis testing, and confidence intervals.

Model Specification in Time Series

Covers the identification and model specification in time series analysis, including AR models and least squares estimation.

Gram-Schmidt Algorithm: Orthogonalization and QR Factorization

Introduces the Gram-Schmidt algorithm, QR factorization, and the method of least squares.

Linear Equations: Least Squares Solution

Explains how to solve linear equations using the least squares method to minimize errors in the system.

Recursive Least-Squares: Weighted Formulation

Covers the Recursive Least-Squares algorithm with weighted formulation for real-time data updating.

Geometry and Least Squares

Discusses the geometry of least squares, exploring row and column perspectives, hyperplanes, projections, residuals, and unique vectors.