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

D. H. Foster, W. F. Bischof

    Research output: Contribution to journalArticlepeer-review


    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. © 1987 Springer-Verlag.
    Original languageEnglish
    Pages (from-to)341-347
    Number of pages6
    JournalBiological cybernetics
    Issue number4-5
    Publication statusPublished - Nov 1987


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