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Reproducible Aggregation of Sample-Split Statistics

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  • David M. Ritzwoller
  • Joseph P. Romano

Abstract

Statistical inference is often simplified by sample-splitting. This simplification comes at the cost of the introduction of randomness not native to the data. We propose a simple procedure for sequentially aggregating statistics constructed with multiple splits of the same sample. The user specifies a bound and a nominal error rate. If the procedure is implemented twice on the same data, the nominal error rate approximates the chance that the results differ by more than the bound. We illustrate the application of the procedure to several widely applied econometric methods.

Suggested Citation

  • David M. Ritzwoller & Joseph P. Romano, 2023. "Reproducible Aggregation of Sample-Split Statistics," Papers 2311.14204, arXiv.org, revised Nov 2024.
    • Handle: RePEc:arx:papers:2311.14204
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    References listed on IDEAS

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    1. Meinshausen, Nicolai & Meier, Lukas & Bühlmann, Peter, 2009. "p-Values for High-Dimensional Regression," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1671-1681.
    2. Nitis Mukhopadhyay & Sujay Datta, 1996. "On sequential fixed-width confidence intervals for the mean and second-order expansions of the associated coverage probabilities," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 48(3), pages 497-507, September.
    3. Victor Chernozhukov & Mert Demirer & Esther Duflo & Iván Fernández-Val, 2018. "Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments, with an Application to Immunization in India," NBER Working Papers 24678, National Bureau of Economic Research, Inc.
    4. Robin Dunn & Aaditya Ramdas & Sivaraman Balakrishnan & Larry Wasserman, 2023. "Gaussian universal likelihood ratio testing," Biometrika, Biometrika Trust, vol. 110(2), pages 319-337.
    5. Jiafeng Chen & David M. Ritzwoller, 2021. "Semiparametric Estimation of Long-Term Treatment Effects," Papers 2107.14405, arXiv.org, revised Aug 2023.
    6. Chen, Jiafeng & Ritzwoller, David M., 2023. "Semiparametric estimation of long-term treatment effects," Journal of Econometrics, Elsevier, vol. 237(2).
    7. Jinyong Hahn, 1998. "On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects," Econometrica, Econometric Society, vol. 66(2), pages 315-332, March.
    8. Jing Lei, 2020. "Cross-Validation With Confidence," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(532), pages 1978-1997, December.
    9. DiCiccio, Cyrus J. & DiCiccio, Thomas J. & Romano, Joseph P., 2020. "Exact tests via multiple data splitting," Statistics & Probability Letters, Elsevier, vol. 166(C).
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