Experimental Designs for Quantal Response Models.
Final rept. 24 Jun 82-23 Jun 83,
WISCONSIN UNIV-MADISON DEPT OF STATISTICS
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Three problems have been studied. A new sequential design procedure has been developed for estimating the percentiles of a quantal response curve which describes the probability of response as a function of stimulus level. It is asymptotically fully efficient and distribution-free. For small samples it outperforms the best Robbins-Monro procedure in an extensive empirical study. The percentages of saving range from 25 to 60. It is shown that, by classifying the outcome of a sensitivity experiment into more than two categories, the parameters in a parametric response curve can be estimated more precisely. The theoretical result is obtained via the Missing Information Principle. The empirical study points out where substantial gains can be achieved. Several procedures for the multiple comparison of all linear contrasts in unbalanced situations are compared both empirically and theoretically. Recommendations for the choice of procedures are given. Author
- Statistics and Probability
- Test Facilities, Equipment and Methods