Resumen
We consider the construction of locally D-optimal designs for a nonlinear, multistage model in which one observes a binary response variable with expected value P(x;?)=H(?0+?1x+?+?kxk). Here H is any twice differentiable distribution function. Our results apply as well to heteroscedastic polynomial regression models, under mild conditions on the efficiency function.

Resumen
We develop criteria that generate robust designs and use such criteria for the construction of designs that insure against possible misspecifications in logistic regression models. The design criteria we propose are different from the classical in that we do not focus on sampling error alone. Instead we use design criteria that account as well for error due to bias engendered by the model misspecification. Our robust designs optimize the average of a function of the sampling error and bias error over a specified misspecification neighbourhood. Examples of robust designs for logistic models are presented, including a case study implementing the methodologies using beetle mortality data.