Open Access: Copula‐based robust optimal block designs


  • Author: Statistics Views
  • Date: 15 July 2020

Each week, we select a recently published Open Access article to feature. This week's article comes from Applied Stochastic Models in Business and Industry and considers copula‐based robust optimal block designs. This article also forms part of the ASMBI Special Issue on Energy Networks and Stochastic Optimization.

The article's abstract is given below, with the full article available to read here.

thumbnail image: Open Access: Copula‐based robust optimal block designs

Rappold, A, Müller, WG, Woods, DC. Copula‐based robust optimal block designs. Appl Stochastic Models Bus Ind. 2020; 36: 210– 219.

Blocking is often used to reduce known variability in designed experiments by collecting together homogeneous experimental units. A common modeling assumption for such experiments is that responses from units within a block are dependent. Accounting for such dependencies in both the design of the experiment and the modeling of the resulting data when the response is not normally distributed can be challenging, particularly in terms of the computation required to find an optimal design. The application of copulas and marginal modeling provides a computationally efficient approach for estimating population‐average treatment effects. Motivated by an experiment from materials testing, we develop and demonstrate designs with blocks of size two using copula models. Such designs are also important in applications ranging from microarray experiments to experiments on human eyes or limbs with naturally occurring blocks of size two. We present a methodology for design selection, make comparisons to existing approaches in the literature, and assess the robustness of the designs to modeling assumptions.

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Published features on are checked for statistical accuracy by a panel from the European Network for Business and Industrial Statistics (ENBIS)   to whom Wiley and express their gratitude. This panel are: Ron Kenett, David Steinberg, Shirley Coleman, Irena Ograjenšek, Fabrizio Ruggeri, Rainer Göb, Philippe Castagliola, Xavier Tort-Martorell, Bart De Ketelaere, Antonio Pievatolo, Martina Vandebroek, Lance Mitchell, Gilbert Saporta, Helmut Waldl and Stelios Psarakis.