Nonlinear mixed-effects model

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Related lectures (32)

Page 2 of 4

Covers iterative weighted least squares, Poisson regression, and Bayesian analysis of spring barley data using mixed models.

Generalized Additive Models: Applications and Techniques

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

Response Surfaces I: LoF and Plans

Explores lack of fit concept and experimental designs for second-degree functions.

Generalized Additive Models: Penalized Iterative Weighted Least Squares

Covers the introduction to generalized additive models and iterative weighted least squares for model checking and smooth fits.

Structure Discovery: Tracing Student Knowledge

Introduces Bayesian Knowledge Tracing, Additive Factors Model, and clustering algorithms for tracing student knowledge and discovering structures.

Natural Cubic Splines

Explains the construction of natural cubic splines, emphasizing smoothness and continuity in the function representation.

IWLS Algorithm: Overview

Covers the IWLS algorithm for obtaining MLEs in regression models and discusses the likelihood ratio statistic and deviance in model fitting.

CMOS Circuits: Metabolites Detection

Explores CMOS circuits for metabolites detection in fixed-voltage cells, covering operational amplifier features, saturation risks, temperature compensation, and current measurement techniques.

Modern Regression: Spring Barley Data

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

Regression Methods: Model Building and Inference

Covers analysis of variance, model building, variable selection, and function estimation in regression methods.