Bayesian optimality criteria provide a robust design strategy to parameter misspecification. We develop an approximate design theory for Bayesian D-optimality for nonlinear regression models with covariates subject to measurement errors. Both maximum likelihood and least squares estimation are studied and explicit characterisations of the Bayesian D-optimal saturated designs for the Michaelis-M...

The asymptotic properties of the least squares estimator of the cusp in some nonlinear nonregular regression models is investigated via the study of the weak convergence of the least squares process generalizing earlier results in Prakasa Rao ( Statist. Prob. Lett. 3 (1985), 15-18).

Switching mode power converters are being extensively applied in different conversion systems. Parameter identification comprises a set of techniques focused on extracting the relevant parameters order to generate accurate discrete simulation models or design enhanced condition diagnosis schemes. This article applies noninvasive optimization approach based nonlinear least squares algorithm dete...