Asymptotic Inference for Optimal Rerandomization Designs

Mårten Schultzberg Uppsala University, Sweden Per Johansson Uppsala University, Sweden; Tsinghua University, China

Statistics Theory and Methods mathscidoc:2206.33004

Open Statistics, 1, (1), 49-58, 2021.1
Recently a computational-based experimental design strategy called rerandomization has been proposed as an alternative or complement to traditional blocked designs. The idea of rerandomization is to remove, from consideration, those allocations with large imbalances in observed covariates according to a balance criterion, and then randomize within the set of acceptable allocations. Based on the Mahalanobis distance criterion for balancing the covariates, we show that asymptotic inference to the population, from which the units in the sample are randomly drawn, is possible using only the set of best, or ‘optimal’, allocations. Finally, we show that for the optimal and near optimal designs, the quite complex asymptotic sampling distribution derived by Li et al. (2018), is well approximated by a normal distribution.
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  title={Asymptotic Inference for Optimal Rerandomization Designs},
  author={Mårten Schultzberg, and Per Johansson},
  booktitle={Open Statistics},
Mårten Schultzberg, and Per Johansson. Asymptotic Inference for Optimal Rerandomization Designs. 2021. Vol. 1. In Open Statistics. pp.49-58.
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