Category
Multivariate statistics
Related lectures (64)
Multilevel Models: Part 2
Explores advanced techniques in multilevel modeling, including fitting separate models, estimating coefficients, and checking residuals for model evaluation.
Regression: Linear Models
Introduces linear regression, generalized linear models, and mixed-effect models for regression analysis.
Modern Regression: Smoothing and Modelling Choices
Explores roughness penalty, band matrices, and Bayesian inference in regression smoothing.
Linear Mixed Model
Covers the linear mixed model, including fixed and random effects, estimation, and inference techniques.
Natural Cubic Splines: Optimization and Penalization
Explores the optimization and penalization of natural cubic splines, including roughness penalties and Bayesian inference.
Modern Regression: Random Effects and Model Checking
Explores random effects, model checking, and nested vs. crossed effects in modern regression modeling.
Generalized Additive Models: Applications and Techniques
Explores Generalized Additive Models, covering basics, smooth functions, penalties, practical examples in R, and linear mixed models.
Modern Regression: Spring Barley Data
Covers iterative weighted least squares, Poisson regression, and Bayesian analysis of spring barley data using mixed models.
Non-Negative Definite Matrices and Covariance Matrices
Covers non-negative definite matrices, covariance matrices, and Principal Component Analysis for optimal dimension reduction.
Modern Regression: Spring Barley Data
Covers inference, weighted least squares, spring barley data analysis, and smoothing techniques.