skip to content
 

Cost constrained optimal designs for regression models with random parameters

Date: 
Tuesday 7th July 2015 - 16:30 to 17:00
Venue: 
INI Seminar Room 1
Abstract: 
I describe various optimization problems related to the design of experiments for regression models with random parameters, aka mixed effect models and population models. In the terms of the latter two different goals can be pursuit: estimation of population parameters and individual parameters. Respectively we have to face two types of optimality criteria and cost constraints. Additional strata appear if one would observe that the following two experimental situations occur in practice: either repeated observations are admissible for a given experimental unit (object or subject), or not. Clinical studies with multiple sites with slightly different treatment outcomes (treatment-by-cite interaction) is an example when repeated and independent observation are possible - a few subjects can on each treatment arm. PK studies may serve as an example when repeated observations cannot be performed - only one observation at the given moment can be performed on a subject. All these caveats lead to the different design problems that I try to link together.
The video for this talk should appear here if JavaScript is enabled.
If it doesn't, something may have gone wrong with our embedded player.
We'll get it fixed as soon as possible.
Presentation Material: 
University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons