The Annals of Statistics

The Jackknife and the Bootstrap for General Stationary Observations

Hans R. Kunsch

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We extend the jackknife and the bootstrap method of estimating standard errors to the case where the observations form a general stationary sequence. We do not attempt a reduction to i.i.d. values. The jackknife calculates the sample variance of replicates of the statistic obtained by omitting each block of $l$ consecutive data once. In the case of the arithmetic mean this is shown to be equivalent to a weighted covariance estimate of the spectral density of the observations at zero. Under appropriate conditions consistency is obtained if $l = l(n) \rightarrow \infty$ and $l(n)/n \rightarrow 0$. General statistics are approximated by an arithmetic mean. In regular cases this approximation determines the asymptotic behavior. Bootstrap replicates are constructed by selecting blocks of length $l$ randomly with replacement among the blocks of observations. The procedures are illustrated by using the sunspot numbers and some simulated data.

Article information

Ann. Statist., Volume 17, Number 3 (1989), 1217-1241.

First available in Project Euclid: 12 April 2007

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Digital Object Identifier

Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier


Primary: 62G05: Estimation
Secondary: 62G15: Tolerance and confidence regions 62M10: Time series, auto-correlation, regression, etc. [See also 91B84]

Variance estimation jackknife bootstrap statistics defined by functionals time series influence function


Kunsch, Hans R. The Jackknife and the Bootstrap for General Stationary Observations. Ann. Statist. 17 (1989), no. 3, 1217--1241. doi:10.1214/aos/1176347265.

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