Name
Abstract | ||
|---|---|---|
This paper presents and illustrates some novel subset design approaches for nonlinear regression models and for linear models where interest lies in a nonlinear function of the model parameters with a special emphasis on models useful in detection drug synergy. These design strategies are particularly useful in situations where currently used subset design procedures fail to provide designs that can be used to fit the model function. Our original design technique is illustrated in conjunction with D-optimality, Bayesian D-optimality and Kiefer's ΨPSkoptimality, and is extended to yield subset designs that take account of curvature. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1016/S1571-0653(04)00558-X | Electronic Notes in Discrete Mathematics |
Keywords | Field | DocType |
Antagonism,,AUC,,Bayesian designs,,Curvature,,Parameter subsets,,ΨPSk-optimality,,Robustness,,Synergy,,Turning-point | Mathematical optimization,Nonlinear system,Curvature,Linear model,Nonlinear regression,Robustness (computer science),Optimal design,Mathematics,Bayesian probability | Journal |
Volume | ISSN | Citations |
15 | 1571-0653 | 1 |
PageRank | References | Authors |
0.63 | 0 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Timothy E. O'Brien | 1 | 1 | 0.96 |