Multivariate statistics

Related lectures (32)

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.

Natural Cubic Splines

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

Nested Model Selection

Explores nested model selection in linear models, comparing models through sums of squares and ANOVA, with practical examples.

Modern Regression: Inference and Models

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

Regression Methods: Model Building and Inference

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

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: Overdispersion and Model Assessment

Explores overdispersion, model assessment, and regression techniques for count data.

Ridge Regression: Penalised Least Squares

Explores Ridge Regression for handling multicollinearity and the LASSO method for model selection.

Panel data: dynamic model with panel effects

Covers the Dynamic Markov model with panel effects, addressing initial condition and endogeneity.

Panel data: serial correlation

Explores the panel effect model with fixed and random effects, discussing estimation challenges and the impact of serial correlation.

Regression Diagnostics

Covers regression diagnostics for linear models, emphasizing the importance of checking assumptions and identifying outliers and influential observations.