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Bootstrap variance estimators for the parameters of small-sample sensory-performance functions

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Abstract

The bootstrap method, due to Bradley Efron, is a powerful, general method for estimating a variance or standard deviation by repeatedly resampling the given set of experimental data. The method is applied here to the problem of estimating the standard deviation of the estimated midpoint and spread of a sensory-performance function based on data sets comprising 15–25 trials. The performance of the bootstrap estimator was assessed in Monte Carlo studies against another general estimator obtained by the classical “combination-of-observations” or incremental method. The bootstrap method proved clearly superior to the incremental method, yielding much smaller percentage biases and much greater efficiencies. Its use in the analysis of sensory-performance data may be particularly appropriate when traditional asymptotic procedures, including the probittransformation approach, become unreliable.

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Authors and Affiliations

  1. Department of Communication and Neuroscience, University of Keele, ST5 5BG, Keele, Staffordshire, England

    D. H. Foster

  2. Alberta Centre for Machine Intelligence and Robotics, University of Alberta, T6G 2E9, Edmonton, Canada

    W. F. Bischof

Authors
  1. D. H. Foster
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  2. W. F. Bischof
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Foster, D.H., Bischof, W.F. Bootstrap variance estimators for the parameters of small-sample sensory-performance functions. Biol. Cybern. 57, 341–347 (1987). https://doi.org/10.1007/BF00338826

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  • DOI: https://doi.org/10.1007/BF00338826

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