Nonlinear mixed-effects model

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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.

Regression Methods: Model Building and Inference

Covers Inference, Model Building, Variable Selection, Robustness, Regularised Regression, Mixed Models, and Regression Methods.

Projection Pursuit Regression: Nonlinear Modeling and Interpretability

Explores Projection Pursuit Regression for nonlinear modeling and the trade-offs with interpretability in neural networks.

Linear Mixed Model

Covers the linear mixed model, including fixed and random effects, estimation, and inference techniques.

Modern Regression: Smoothing and Modelling Choices

Explores roughness penalty, band matrices, and Bayesian inference in regression smoothing.

Natural Cubic Splines: Optimization and Penalization

Explores the optimization and penalization of natural cubic splines, including roughness penalties and Bayesian inference.

Modern Regression: Inference and Models

Covers iterative weighted least squares, model checking, and generalized linear models in regression analysis.

Inference: Model Checking

Covers iterative weighted least squares, generalized linear models, and model checking.

Structure Discovery: Machine Learning for Behavioral Data

Explores Bayesian Knowledge Tracing, Generalized Linear Models, and clustering algorithms for structure discovery in behavioral data.