A convex approach to optimum design of experiments with correlated observations

2021/03/04 by Andrej Pázman, Pázman, Andrej, Markus Hainy +3
Computer Science · Decision Sciences · #62K05 #Advanced Multi-Objective Optimization Algorithms #Advanced Statistical Process Monitoring #FOS: Computer and information sciences #FOS: Mathematics #Methodology (stat.ME) #Optimal Experimental Design Methods #Statistics Theory (math.ST)

paper · pdf · doi:10.48550/arxiv.2103.02989

openalex publication_date 2021/03/04 · openalex created_date 2022/07/25 · openalex updated_date 2026/08/01

Abstract

Optimal design of experiments for correlated processes is an increasingly relevant and active research topic. Present methods have restricted possibilities to judge their quality. To fill this gap, we complement the virtual noise approach by a convex formulation leading to an equivalence theorem comparable to the uncorrelated case and to an algorithm giving an upper performance bound against which alternative design methods can be judged. Moreover, a method for generating exact designs follows naturally. We exclusively consider estimation problems on a finite design space with a fixed number of elements. A comparison on some classical examples from the literature as well as a real application is provided.

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