Linear mixed model (LMM) methodology is a powerful technology to analyze models containing both the fixed and random effects. The model was first proposed to estimate genetic parameters for unbalanced ...

This paper develops a class of models to deal with missing data from longitudinal studies. We assume that separate models for the primary response and missingness (e.g., number of missed visits) are ...

This paper presents an attempt to explicate some common features of several superficially diverse techniques of data analysis and to indicate how the logic of a single abstract model is relevant to ...

This section provides an overview of a likelihood-based approach to general linear mixed models. This approach simplifies and unifies many common statistical analyses, including those involving ...

This course is compulsory on the MSc in Statistics (Social Statistics) and MSc in Statistics (Social Statistics) (Research). This course is available on the MPhil/PhD in Statistics, MSc in Data ...

You construct a generalized linear model by deciding on response and explanatory variables for your data and choosing an appropriate link function and response probability distribution. Some examples ...

Linear models, generalized linear models, and nonlinear models are examples of parametric regression models because we know the function that describes the relationship between the response and ...

Researchers from many fields can benefit from applied knowledge of general linear models. This class of models includes the t-test (paired and two sample), regression, ANOVA, and ANCOVA. Like all ...

You have two options when enrolling in this course: (1) Instructor support. Reach out to your instructor over a 1-month period to get help as you work through prerecorded lectures, problem sets, and ...