This study compared the performance of a local and three robust optimality criteria in terms of the standard error for a one-parameter and a two-parameter nonlinear model with uncertainty in the parameter values. The designs were also compared in conditions where there was misspecification in the prior parameter distribution. The impact of different correlation between parameters on the optimal design was examined in the two-parameter model. The designs and standard errors were solved analytically whenever possible and numerically otherwise
ABSTRACT: This paper points out that so-called optimal designs for non linear regression models are ...
This paper proposes a method to compare the performances of different methods for robust design opti...
Robust parameter design is one of the most important tools for quality improvement. Quality problems...
This study compared the performance of a local and three robust optimality criteria in terms of the ...
iii Assumptions are usually made when optimising design for an experiment. Unexpected departure from...
Experimental designs for nonlinear problems have to a large extent relied on optimality criteria ori...
This paper is concerned with the statistical properties of experimental designs where the factor lev...
A new robust design optimization method to automatically search multiple optimal solutions and to es...
Many drug concentration-effect relationships are described by nonlinear sigmoid models. The 4-parame...
Many drug concentration-effect relationships are described by nonlinear sigmoid models. The 4-parame...
Assumptions are usually made when optimising design for an experiment. Unexpected departure from the...
Parameter design optimization that involves two or more responses of products or processes is a well...
Usually, in the Theory of Optimal Experimental Design the model is assumed to be known at the design...
Usually, in the Theory of Optimal Experimental Design the model is assumed to be known at the design...
Abstract: The main drawback of the optimal design approach is that it assumes the statistical model ...
ABSTRACT: This paper points out that so-called optimal designs for non linear regression models are ...
This paper proposes a method to compare the performances of different methods for robust design opti...
Robust parameter design is one of the most important tools for quality improvement. Quality problems...
This study compared the performance of a local and three robust optimality criteria in terms of the ...
iii Assumptions are usually made when optimising design for an experiment. Unexpected departure from...
Experimental designs for nonlinear problems have to a large extent relied on optimality criteria ori...
This paper is concerned with the statistical properties of experimental designs where the factor lev...
A new robust design optimization method to automatically search multiple optimal solutions and to es...
Many drug concentration-effect relationships are described by nonlinear sigmoid models. The 4-parame...
Many drug concentration-effect relationships are described by nonlinear sigmoid models. The 4-parame...
Assumptions are usually made when optimising design for an experiment. Unexpected departure from the...
Parameter design optimization that involves two or more responses of products or processes is a well...
Usually, in the Theory of Optimal Experimental Design the model is assumed to be known at the design...
Usually, in the Theory of Optimal Experimental Design the model is assumed to be known at the design...
Abstract: The main drawback of the optimal design approach is that it assumes the statistical model ...
ABSTRACT: This paper points out that so-called optimal designs for non linear regression models are ...
This paper proposes a method to compare the performances of different methods for robust design opti...
Robust parameter design is one of the most important tools for quality improvement. Quality problems...