Optimal Designs
for a Beta GLM with Two Predictors
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This book covers the construction of optimal experimental designs for Beta regression models using two predictors without interaction term. D-criterion is used as basis of optimality. Keeping in view the analytical complexity of the problem First Order Exchange Algorithm has been used to obtain locally optimal designs. These designs have been constructed under different conditions of parameter values, like positive, negative and mixture of positive and negative values. Robustness of optimal designs under parameter misspecification has also been investigated; and designs have been compared with...
This book covers the construction of optimal experimental designs for Beta regression models using two predictors without interaction term. D-criterion is used as basis of optimality. Keeping in view the analytical complexity of the problem First Order Exchange Algorithm has been used to obtain locally optimal designs. These designs have been constructed under different conditions of parameter values, like positive, negative and mixture of positive and negative values. Robustness of optimal designs under parameter misspecification has also been investigated; and designs have been compared with commonly used equi-weighted designs in order to study the gain in efficiency. Use of the Efficient Rounding Procedure for obtaining exact designs by rounding off the design weights has been discussed and demonstrated through examples. The optimal designs for a variety of parameter settings are provided in appendices. These designs may be used in real life situations where appropriate.
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