Skip to main content
Graph
Search
fr
en
Login
Search
All
Categories
Concepts
Courses
Lectures
MOOCs
People
Quizes
Exercises
Publications
Startups
Units
Show all results for
Home
Lecture
Modern Regression: Smoothing and Modelling Choices
Graph Chatbot
Related lectures (46)
Regression: Linear Models
Introduces linear regression, generalized linear models, and mixed-effect models for regression analysis.
Linear Regression: Statistical Inference and Regularization
Covers the probabilistic model for linear regression and the importance of regularization techniques.
Linear Models: Ridge, OLS and LASSO
Covers linear models like Ridge, OLS, and LASSO, explaining singular values and regression analysis.
Probabilistic Models for Linear Regression
Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.
Probabilistic Linear Regression
Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
Neural Networks: Multilayer Perceptrons
Covers Multilayer Perceptrons, artificial neurons, activation functions, matrix notation, flexibility, regularization, regression, and classification tasks.
Nonparametric Statistics: Bayesian Approach
Explores non-parametric statistics, Bayesian methods, and linear regression with a focus on kernel density estimation and posterior distribution.
Linear Regression: Estimation and Inference
Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.
Splines: Fundamentals and Applications
Explores B-splines, natural cubic splines, and smoothing splines in regression problems and their practical applications.
Regression Methods: Model Building and Inference
Log in to Mediaspace to watch this video
Covers Inference, Model Building, Variable Selection, Robustness, Regularised Regression, Mixed Models, and Regression Methods.
Multilevel Models: Part 2
Log in to Mediaspace to watch this video
Explores advanced techniques in multilevel modeling, including fitting separate models, estimating coefficients, and checking residuals for model evaluation.
Modern Regression: Random Effects and Model Checking
Log in to Mediaspace to watch this video
Explores random effects, model checking, and nested vs. crossed effects in modern regression modeling.
Modern Regression: Spring Barley Data
Log in to Mediaspace to watch this video
Covers iterative weighted least squares, Poisson regression, and Bayesian analysis of spring barley data using mixed models.
Natural Cubic Splines: Optimization and Penalization
Log in to Mediaspace to watch this video
Explores the optimization and penalization of natural cubic splines, including roughness penalties and Bayesian inference.
Regression Methods: Model Building and Inference
Log in to Mediaspace to watch this video
Covers analysis of variance, model building, variable selection, and function estimation in regression methods.
Modern Regression: Inference and Models
Log in to Mediaspace to watch this video
Covers iterative weighted least squares, model checking, and generalized linear models in regression analysis.
Modern Regression: Overdispersion and Model Assessment
Log in to Mediaspace to watch this video
Explores overdispersion, model assessment, and regression techniques for count data.
Linear Regression: Basics and Estimation
Log in to Mediaspace to watch this video
Covers the basics of linear regression and how to solve estimation problems using least squares and matrix notation.
Natural Cubic Splines
Log in to Mediaspace to watch this video
Explains the construction of natural cubic splines, emphasizing smoothness and continuity in the function representation.
Linear Mixed Model
Log in to Mediaspace to watch this video
Covers the linear mixed model, including fixed and random effects, estimation, and inference techniques.
Previous
Page 1 of 3
Next
