PhD Course: Mixed Models

Seats are still available on this scientific skills course aiming to provide the basic tools for using Mixed Models (including Gaussian Linear Mixed Models, Models for Repeated Measures, Generalized Linear Mixed Models and simple Multivariate Generalized Linear Mixed Models)

2018.08.17 | Maia Høyer Monod

Aarhus University

Aarhus University

In September, Senior Researcher Rodrigo Labouriau from Department of Mathematics invites PhD students to join him in a 3-day course on the basic tools in using mixed models for statistical data analysis.

The course starts by revising the basic theory of Gaussian Linear Models (i.e. linear models based on the normal distribution); these models are extended to the class Gaussian Linear Mixed Models that incorporate random components representing structures of dependency commonly found in dependent experimental and observational data. Next, the class of generalized linear models are presented, which allow to model non-Gaussian responses (e.g. binomial, Poisson, Gamma and Inverse Gaussian distributed responses) and non-linear relationships with explanatory variables. Finally, simple multivariate generalized linear mixed models, which are presented and discussed. These last models allows for modelling several responses (possibly of different nature) simultaneously. In all cases, emphasis is put on application; mathematical and theoretical details will not be emphasized but instead the conscientious use of the models studied will be aimed.  

Participants are required to master some basic statistical techniques. The course will use the software R as a tool, but it is NOT a course on R. It will be assumed that participants already know the basic notions of R programming. Detailed prerequisites are outlined on the course webpage (see link below).

Time and place:

Tuesdays, 4, 11 and 25 September 2018 from 9:00-16:00 at Aarhus University. Room will be announced.

For more information:

Deadline for registration is 24 August 2018. For more information and registration, please see the course website.

Talent development, PhD students
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Revised 15.11.2018