Reproducible Aggregation of Sample-Split Statistics

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Abstract/Contents

Abstract: Statistical inference is often simplified by sample-splitting. This simplification comes at the cost of the introduction of randomness that is 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 provide a non-asymptotic analysis of the accuracy of the nominal error rate and illustrate the application of the procedure to several widely applied statistical methods.

Type of resource	text
Publication date	November 30, 2023

Author	Ritzwoller, D.M.
Author	Romano, J.P.

DOI	https://doi.org/10.25740/jt589nw1637
Location	https://purl.stanford.edu/jt589nw1637

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License: This work is licensed under a Creative Commons Attribution Non Commercial No Derivatives 4.0 International license (CC BY-NC-ND).

Preferred citation: Ritzwoller, D. and Romano, J. (2023). Reproducible Aggregation of Sample-Split Statistics. Department of Statistics Technical Report, Stanford University. Available from the Stanford Digital Repository at https://purl.stanford.edu/jt589nw1637. https://doi.org/10.25740/jt589nw1637.

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