University of Cambridge > Talks.cam > Isaac Newton Institute Seminar Series > On connections between orthogonal arrays and D-optimal designs for certain generalized linear models with group effects

On connections between orthogonal arrays and D-optimal designs for certain generalized linear models with group effects

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact webseminars.

Design and Analysis of Experiments

We consider generalized linear models in which the linear predictor depends on a single quantitative variable and on multiple factorial effects. For each level combination of the factorial effects (or run, for short), a design specifies the number of values of the quantitative variable (or values, for short) that are to be used, and also specifies what those values are. Moreover, a design informs us for each combinations of runs and values what proportion of times that it should be used. Stufken and Yang (2012, Statistica Sinica) obtained complete class results for locally optimal designs for such models. The complete classes that they found consisted of designs with at most two values for each run. Many optimal designs found numerically in these complete classes turned out to have precisely two values for each run, resulting in designs with a large support size. Focusing on D-optimality, we show that under certain assumptions for the linear predictor, optimal designs with smaller support sizes can be found through the use of orthogonal arrays. This work is joined with Xijue Tan, and was part of his PhD dissertation at the University of Georgia.

This talk is part of the Isaac Newton Institute Seminar Series series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2019 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity