Optimal Experimental Design and Statistical Modeling

Dear Colleagues,

It is well known that the design and analysis of experiments have been used to improve estimates and predictions in empirical processes. It is remarkable to note the utility of measuring the efficiency of the implemented designs in practice and of providing more competitive designs for the optimal use of available resources. Optimal Experimental Design (OED) is a discipline that addresses this problem from different points of view. An important feature of optimal design is that it estimates statistical models with fewer experimental runs and thereby provides valid and precise statistical inference at a minimal cost given the constraints of the study. In the last few years, researchers have applied the ideas of their methodological developments of OED to different multidisciplinary areas such as engineering, biomedical and pharmaceutical research, spatial sampling, epidemiological studies, social studies, and drug development. The Big Data boom has made an impact in this field in what is referred to as active learning. Thus, there is a challenge to develop a basis for suggesting designs in nonstandard situations.

This Special Issue will focus on contributions to the statistical theory and practice of design. The main objective is to present the solutions that modern experimental design brings to the major challenges that arise in the different disciplines. Topics include but are not limited to:

• Algorithms for the design of experiments
• Discrimination
• Robustness of experimental designs
• Reliability/Survival experimental designs
• Machine learning methodologies
• Experiments with mixtures
• Designs for nonlinear models
• Split-plot designs
• Computer experiments
• Multi-objective optimal design
• Bayesian experimental designs, etc.

Prof. Dr. Raul Martin Martin
Prof. Dr. Wengkee Wong
Guest Editors

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