Department of Statistics

Department of Statistics

skip to events

calendar tabs

  •  All 
  • Grid
  • Month
  • Week
  • Day
  • (Selected tab) Detail

Event Detail Information

Event Detail Information

Universally Optimal Crossover Designs under Subject Dropout

Speaker Wei Zheng (IUPUI)
Date Mar 28, 2013
Time 4:00 pm - 5:00 pm  
Location 165 Everitt
Sponsor Statistics Department
Event type Seminar
Views 1402
Crossover designs are designs of experiments in order to compare effects of different treatments by applying them to a number of subjects during a sequence of periods. That means each subject will have repeated measurements based on different treatments. Subject dropout is very common in practical applications of crossover designs. However, there is very limited literature of experimental design taking this into account. Optimality results have not yet been well established due to complexity of the problem. This paper establishes feasible necessary and sufficient conditions for a crossover design to be universally optimal in approximate design theory under the presence of subject dropout. These conditions are essentially linear equations with respect to proportions of all possible treatment sequences being applied to subjects and hence they can be easily solved. A general algorithm is proposed to derive exact designs which are shown to be efficient and robust.
link for robots only