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Analysis of tidal data via the blockwise bootstrap

Author

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  • Michael Sherman
  • F. Michael Speed
  • F. Michael Speed

Abstract

We analyze tidal data from Port Mansfield, TX, using Kunsch's blockwise bootstrap in the regression setting. In particular, we estimate the variability of parameter estimates in a harmonic analysis via block subsampling of residuals from a least-squares fit. We see that naive least-squares variance estimates can be either too large or too small, depending on the strength of correlation and the design matrix. We argue that the block bootstrap is a simple, omnibus method of accounting for correlation in a regression model with correlated errors.

Suggested Citation

  • Michael Sherman & F. Michael Speed & F. Michael Speed, 1998. "Analysis of tidal data via the blockwise bootstrap," Journal of Applied Statistics, Taylor & Francis Journals, vol. 25(3), pages 333-340.
    • Handle: RePEc:taf:japsta:v:25:y:1998:i:3:p:333-340
    DOI: 10.1080/02664769823061
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    Cited by:

    1. Livina, V.N. & Lohmann, G. & Mudelsee, M. & Lenton, T.M., 2013. "Forecasting the underlying potential governing the time series of a dynamical system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(18), pages 3891-3902.

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