Assessing Response´s Bias, Quality of Predictions, and Robustness in Multiresponse Problems

Research output: Chapter in Book/Report/Conference proceedingChapter

7 Citations (Scopus)

Abstract

Optimization measures for evaluating compromise solutions in multiresponse problems formulated in the Response Surface Methodology framework are proposed. The measures take into account the desired properties of responses at optimal variable settings, namely, the bias, quality of predictions and robustness, which allow the analyst to achieve compromise solutions of interest and feasible in practice, namely in the case of a method that does not consider in the objective function the responses' variance level and correlation information is used. Two examples from the literature show the utility of the proposed measures.
Original languageUnknown
Title of host publicationElectrical Engineering and Applied Computing
EditorsSio-Iong Ao, Len Gelman
Place of PublicationDordrecht
PublisherSpringer Netherlands
Pages445-457
ISBN (Print)978-94-007-1191-4
Publication statusPublished - 1 Jan 2011

Publication series

NameLecture Notes in Electrical Engineering
PublisherSpringer Netherlands
ISSN (Print)1876-1100

Cite this

Pereira, Z. P. D. P. S. L. (2011). Assessing Response´s Bias, Quality of Predictions, and Robustness in Multiresponse Problems. In S-I. Ao, & L. Gelman (Eds.), Electrical Engineering and Applied Computing (pp. 445-457). (Lecture Notes in Electrical Engineering). Springer Netherlands.