Better experimental design by hybridizing binary matching with imbalance optimization-Reference-Cited by-同舟云学术

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Better experimental design by hybridizing binary matching with imbalance optimization

Published:2022-02-02 Issue:1 Volume:51 Page:275-292
ISSN:0319-5724
Container-title:Canadian Journal of Statistics
language:en
Short-container-title:Can J Statistics

Author:

Krieger Abba M.¹,Azriel David A.²,Kapelner Adam³^ORCID

Affiliation:

1. Department of Statistics The Wharton School of the University of Pennsylvania Philadelphia PA U.S.A

2. Faculty of Industrial Engineering and Management The Technion Technion City Israel

3. Department of Mathematics Queens College, CUNY Queens NY U.S.A

Publisher

Wiley

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cjs.11685

Reference36 articles.

1. Bias-Corrected Matching Estimators for Average Treatment Effects

2. The Validity of Comparative Experiments

3. Beck C. Lu B. &Greevy R.(2016).nbpMatching: Functions for optimal non‐bipartite matching. R package version 1.5.1.

4. The Power of Optimization Over Randomization in Designing Experiments Involving Small Samples

5. Ridge rerandomization: An experimental design strategy in the presence of covariate collinearity

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. The role of pairwise matching in experimental design for an incidence outcome;Australian & New Zealand Journal of Statistics;2023-11-27

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