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Stationary Jackknife

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  • Weilian Zhou
  • Soumendra Lahiri

Abstract

Variance estimation is an important aspect in statistical inference, especially in the dependent data situations. Resampling methods are ideal for solving this problem since these do not require restrictive distributional assumptions. In this paper, we develop a novel resampling method in the Jackknife family called the stationary jackknife. It can be used to estimate the variance of a statistic in the cases where observations are from a general stationary sequence. Unlike the moving block jackknife, the stationary jackknife computes the jackknife replication by deleting a variable length block and the length has a truncated geometric distribution. Under appropriate assumptions, we can show the stationary jackknife variance estimator is a consistent estimator for the case of the sample mean and, more generally, for a class of nonlinear statistics. Further, the stationary jackknife is shown to provide reasonable variance estimation for a wider range of expected block lengths when compared with the moving block jackknife by simulation.

Suggested Citation

  • Weilian Zhou & Soumendra Lahiri, 2024. "Stationary Jackknife," Journal of Time Series Analysis, Wiley Blackwell, vol. 45(3), pages 333-360, May.
    • Handle: RePEc:bla:jtsera:v:45:y:2024:i:3:p:333-360
    DOI: 10.1111/jtsa.12714
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    References listed on IDEAS

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    1. Park, Daesu & Willemain, Thomas R., 1999. "The threshold bootstrap and threshold jackknife," Computational Statistics & Data Analysis, Elsevier, vol. 31(2), pages 187-202, August.
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