Lectures related to Mixed model

Multilevel Models: Part 2

Explores advanced techniques in multilevel modeling, including fitting separate models, estimating coefficients, and checking residuals for model evaluation.

Linear Mixed Model

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

Data-Driven Modeling in Neuroscience: Meenakshi Khosla

By Meenakshi Khosla explores data-driven modeling in large-scale naturalistic neuroscience, focusing on brain activity representation and computational models.

Modern Regression: Smoothing and Modelling Choices

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

Multilevel Models: Understanding Nested Data Structures

Delves into multilevel models, emphasizing nested data structures and intra-class correlation, and explores random-intercept and random-slope models.

Regression Methods: Model Building and Inference

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

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.

Model Selection: Non-Nested Model Selection

Explores model selection, criteria, bias/variance tradeoff, and cross-validation methods.

Regression: Linear Models

Introduces linear regression, generalized linear models, and mixed-effect models for regression analysis.

Modern Regression: Spring Barley Data

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

Optimal Control: Equivalent Problems

Discusses optimal control with a focus on three equivalent problems related to elastic strings.

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.

Regression Methods: Model Building and Inference

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

Inference and Mixed Models

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

Generalized Additive Models: Applications and Techniques

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

Natural Cubic Splines

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

Generalized Linear Models: Exponential Families and Model Construction

Covers exponential families, model construction, and canonical link functions in Generalized Linear Models.

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.