Linear Mixed Model

Related lectures (32)

Page 2 of 4

Covers inference, weighted least squares, spring barley data analysis, and smoothing techniques.

Covers regression with exponential family responses using Generalised Linear Models.

Covers point estimation, confidence intervals, and hypothesis testing for smooth functions using mixed models and spline smoothing.

Explores Generalized Additive Models, covering basics, smooth functions, penalties, practical examples in R, and linear mixed models.

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

Covers maximum likelihood theory, applications, and hypothesis testing principles in econometrics.

Explains the decomposition of total sum of squares, model adjustment, and parameter estimation in linear regression.

Covers confidence intervals, R2, and examples on cement heat evolution and car horsepower-MPG relationships.

Explores logistic regression analysis of horseshoe crab data, focusing on odds ratio interpretation and model fitting.

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