Efficient experimental designs for sigmoidal growth models (original) (raw)

Abstract

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Efficient experimental designs for sigmoidal growth models are vital for enhancing the quality of statistical inference across various applications. This paper addresses the challenge of formulating robust designs resilient to parameter misspecification in sigmoidal regression, particularly in nonlinear contexts. By comparing different optimal designs, the authors aim to propose methodologies that not only improve model testing but also provide adaptable frameworks for practical implementation in experimental settings.

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