Accession Number : ADA134573

Title :   Efficient Model-Based Sequential Designs for Sensitivity Experiments.

Descriptive Note : Technical summary rept.,

Corporate Author : WISCONSIN UNIV-MADISON MATHEMATICS RESEARCH CENTER

Personal Author(s) : Wu,C F Jeff

PDF Url : ADA134573

Report Date : Sep 1983

Pagination or Media Count : 31

Abstract : A sequential design 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. Its efficiency in terms of saving the number of runs and its robustness against the distributional assumption are demonstrated heuristically and in a simulation study. A linear approximation to the logit-MLE version of the proposed sequential design is shown to be equivalent to an asymptotically optimal stochastic approximation method, thereby providing a large sample justification. For sample size between 12 and 35, the simulation study shows that the logit-MLE version of the general sequential procedure substantially outperforms an adaptive (and asymptotically optimal) version of the Robbins-Monro method, which in turn outperforms the nonadaptive Robbins-Munro and Up-and-Down methods. A nonparametric sequential design, via the Spearman-Karber estimator, for estimating the median is also proposed. (Author)

Descriptors :   *Numerical methods and procedures, *Experimental design, *Mathematical models, Sensitivity, Sequences(Mathematics), Stochastic processes, Approximation(Mathematics), Linearity, Optimization, Parametric analysis, Response, Curvature

Subject Categories : Statistics and Probability

Distribution Statement : APPROVED FOR PUBLIC RELEASE