Analysis of tidal data via the blockwise bootstrap

Michael Sherman, F. Michael Speed and F. Michael Speed

Journal of Applied Statistics, 1998, vol. 25, issue 3, 333-340

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.

Date: 1998
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DOI: 10.1080/02664769823061

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