LOF of logistic GEE models and cost efficient Bayesian optimal designs for nonlinear combinations of parameters in nonlinear regression models
Nonlinear regression models are widely used in medical science. Before the models can be fit and parameters interpreted, researchers need to decide which design points in a prespecified design space should be included in the experiment. Careful choices at this stage will lead to efficient usage of limited resources. We proposed a cost efficient Bayesian optimal design method for nonlinear combinations of parameters in a nonlinear model with quantitative predictors. An R package was developed to implement the proposed method.
School:Kansas State University
School Location:USA - Kansas
Source Type:Master's Thesis
Keywords:bayesian optimal design nonlinear lack of fit gee cost statistics 0463
Date of Publication:01/01/2008