Model-robust and model-sensitive designs
Copyright policy )Model-robust and model-sensitive designs
The main drawback of the optimal design approach is that it assumes the statistical model is known. To overcome this problem, a new approach to reduce the dependency on the assumed model is proposed. The approach takes into account the model uncertainty by incorporating the bias in the design criterion and the ability to test for lack-of-fit. Several new designs are derived and compared to the alternatives available from the literature
- University College London United Kingdom
- KU Leuven Belgium
- Loyola University Chicago United States
- University of Chicago United States
- National Research Institute for Agriculture, Food and Environment France
D-optimality, bias, Linear regression; mixed models, model-sensitive, model-discrimination, A-optimality, lack-of-fit, [STAT.CO] Statistics [stat]/Computation [stat.CO], Optimal statistical designs, PRECISION, D.OPTIMALITY, A-OPTIMALITY, BIAS, MODEL-ROBUSTNESS, precision, Robustness and adaptive procedures (parametric inference), model-robustness, LACK-OF-FIT, MODEL-SENSITIVE, [STAT.CO]Statistics [stat]/Computation [stat.CO], MODEL-DISCRIMINATION
D-optimality, bias, Linear regression; mixed models, model-sensitive, model-discrimination, A-optimality, lack-of-fit, [STAT.CO] Statistics [stat]/Computation [stat.CO], Optimal statistical designs, PRECISION, D.OPTIMALITY, A-OPTIMALITY, BIAS, MODEL-ROBUSTNESS, precision, Robustness and adaptive procedures (parametric inference), model-robustness, LACK-OF-FIT, MODEL-SENSITIVE, [STAT.CO]Statistics [stat]/Computation [stat.CO], MODEL-DISCRIMINATION
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