Influence of local values on cD-optimal designs for logistic models
Journal Title: TecnoLógicas - Year 2018, Vol 21, Issue 43
Abstract
When experimentsare designed, it is uncommon to use criteria to determine the treatments and number of replicates that should be conducted to properly estimate the parametersin the model under study. This is mainly due to a lack of knowledge of said criteria and, in many other cases, to the difficulty of interpreting them. Optimal designs try to overcome this difficulty by providing optimal experimental conditionsandfactor levels whose response should be evaluated in order to improve the quality of the statistical inference at a lowercost. Said designsalso useoptimality criteria, which are functions of the Fisher-information matrix. One of the most common estimationproblemsin nonlinear models is specifying local values for the parameters of the model, which is necessary to minimicethe optimalitycriterion. Thiswork examines the robustness of optimal designs obtained from logistic modelswhen perturbations in the local valuesof the parameters are considered. The objective is to provide researchers with a wide range of action to select local values while guaranteeing that the efficiency of the resulting optimal designsis not considerably compromised compared to reference values.For that purpose, based on the data of an example, the efficiency of each resulting design was contrasted with the efficiency of unperturbed values. Furthermore, cD-optimaldesigns were created to estimate the logit variance. The impact of said perturbations on local cD-optimal designs resulted in an efficiency of approximately 70%, with a range of 0.04 of perturbation above the referencevalue.
Authors and Affiliations
Víctor Ignacio López-Ríos, David Felipe Sosa-Palacio
Keywords
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- EP ID EP401336
- DOI 10.22430/22565337.1062
- Views 90
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