Average calibration of high-dimensional jackknife empirical likelihood

Mengdong Shang; Xia Chen; Weimin Yang; Li Yan; Mengdong Shang; Xia Chen; Weimin Yang; Li Yan

doi:10.3934/era.2026241

Electronic Research Archive

2026, Volume 34, Issue 8: 5395-5420. doi: 10.3934/era.2026241

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Research article

Average calibration of high-dimensional jackknife empirical likelihood

School of Mathematics and Statistics, Shaanxi Normal University, Xi'an 710119, China

Received: 20 April 2026 Revised: 10 June 2026 Accepted: 22 June 2026 Published: 29 June 2026

This paper investigates calibration of the jackknife empirical likelihood for $ U $-statistics and $ U $-shaped estimating equations. By adjusting the mean and variance of the asymptotic distribution, we propose two calibration methods that control the Type I error rate in high-dimensional settings. To overcome their limitations, we develop an average calibration method for the high-dimensional jackknife empirical likelihood. Numerical results show that this average calibration significantly outperforms standard approaches.
- jackknife empirical likelihood,
- high-dimensional data,
- $ U $-statistics,
- $ U $-shaped estimating equation
Citation: Mengdong Shang, Xia Chen, Weimin Yang, Li Yan. Average calibration of high-dimensional jackknife empirical likelihood[J]. Electronic Research Archive, 2026, 34(8): 5395-5420. doi: 10.3934/era.2026241

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Abstract

This paper investigates calibration of the jackknife empirical likelihood for $ U $-statistics and $ U $-shaped estimating equations. By adjusting the mean and variance of the asymptotic distribution, we propose two calibration methods that control the Type I error rate in high-dimensional settings. To overcome their limitations, we develop an average calibration method for the high-dimensional jackknife empirical likelihood. Numerical results show that this average calibration significantly outperforms standard approaches.

References

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