Efficient Sequential Designs with Binary Data

Abstract

A class of sequential designs for estimating the percentiles of a quantal response curve is proposed. Its updating rule is based on an efficient summary of all the data available via a parametric model. The logit-MLE version of the proposed designs can be viewed as a natural analogue of the Robbins-Monro procedure in the case of binary data. It is shown to be asymptotically consistent, distribution-free and optimal via its connection with the latter procedure. For certain choices of initial designs the proposed method performs very well in a simulation study for sample sizes up to 35. A nonparametric sequential design, via the Spearman-Kerber estimator, for estimating the median is also proposed.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1984
Accession Number
ADA142904

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  • Chengfa Wu

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  • University of Wisconsin–Madison

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  • Mathematics

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