Galecki – Linear Mixed-Effects Models using R

Argomenti del programma: Program : Introduction: a short recap on linear models for independent data The case of heterogeneous variance Linear fixed-effects models for correlated data Linear mixed-effects models Nonparametric regression models Spatial statistics: kriging A brief introduction to Dynamic Linear Models and Kalman filtering A brief introduction to Markov Models and Hidden Markov Models All methods will be illustrated using…

Argomenti del programma: The topics covered by the 8 CFU version of the course are the following: 1) The essence of statistical learning. The regression function: local methods and the curse of dimensionality. Structured models. Test error, training error and expected test error. The bias variance tradeoff. Generalization error and Expected generalization error. Bias-variance tradeoff.

Argomenti del programma: The topics covered by the 8 CFU version of the course are the following: 1) The essence of statistical learning. The regression function: local methods and the curse of dimensionality. Structured models. Test error, training error and expected test error. The bias variance tradeoff. Generalization error and Expected generalization error. Bias-variance tradeoff.