The Annals of Statistics

Sieve bootstrap for smoothing in nonstationary time series

Peter Bühlmann

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Abstract

We propose a sieve bootstrap procedure for time series with a deterministic trend. The sieve for constructing the bootstrap is based on nonparametric trend estimation and autoregressive approximation for some noise process. The bootstrap scheme itself does i.i.d. resampling of estimated innovations from fitted autoregressive models.

We show the validity and indicate second-order correctness of such sieve bootstrap approximations for the limiting distribution of nonparametric linear smoothers. The resampling can then be used to construct nonparametric confidence intervals for the underlying trend. In particular, we show asymptotic validity for constructing confidence bands which are simultaneously within a neighborhood of size in the order of the smoothing bandwidth.

Our resampling procedure yields satisfactory results in a simulation study for finite sample sizes. We also apply it to the longest series of total ozone measurements from Arosa (Switzerland) and find a significant decreasing trend.

Article information

Source
Ann. Statist., Volume 26, Number 1 (1998), 48-83.

Dates
First available in Project Euclid: 28 August 2002

Permanent link to this document
https://projecteuclid.org/euclid.aos/1030563978

Digital Object Identifier
doi:10.1214/aos/1030563978

Mathematical Reviews number (MathSciNet)
MR1611804

Zentralblatt MATH identifier
0934.62039

Subjects
Primary: 62G09: Resampling methods
Secondary: 62G07: Density estimation 62M10: Time series, auto-correlation, regression, etc. [See also 91B84]

Citation

Bühlmann, Peter. Sieve bootstrap for smoothing in nonstationary time series. Ann. Statist. 26 (1998), no. 1, 48--83. doi:10.1214/aos/1030563978. https://projecteuclid.org/euclid.aos/1030563978


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