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A Comparison of Methods of Inference in Randomized Experiments from a Restricted Set of Allocations
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.ORCID iD: 0000-0001-6140-9123
2019 (English)Report (Other academic)
Abstract [en]

Rerandomization is a strategy of increasing eciency as compared to complete randomization. The idea with rerandomization is that of removing allocations with imbalance in the observed covariates and then randomizing within the set of allocations with balance in these covariates. Standard asymptotic inference based on mean dierence estimator is however conservative after rerandomization. Given a Mahalanobis distancecriterion for removing imbalanced allocations, Li et al. (2018) derived the asymptotic distribution of the mean dierence estimator and suggesteda consistent estimator of its variance. This paper discusses several alternative methods of inference under rerandomization, and compare theirperformance with that of the method in Li et al. (2018) through a large Monte Carlo simulation. We conclude that some of the methods work better for small or moderate sample sized experiments than the method in Li et al. (2018).

Place, publisher, year, edition, pages
2019. , p. 23
Series
Working paper / Department of Statistics, Uppsala University ; 5
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:uu:diva-396457OAI: oai:DiVA.org:uu-396457DiVA, id: diva2:1367846
Available from: 2019-11-05 Created: 2019-11-05 Last updated: 2019-11-07Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • vancouver
  • Other style
More styles
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  • de-DE
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  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
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  • asciidoc
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