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The Cubic Logistic Model for Quantal Assay Data

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  • Byron J. T. Morgan

Abstract

Three‐ and four‐parameter models have recently been proposed for quantal assay data. These models are useful for judging whether the fit of simpler standard models, such as the logit, can be improved; better fits could result in better determination of extreme dose levels. However, a disadvantage of these new models is that they are often difficult to fit to data, and so are unlikely to be widely used. One of these models is well‐approximated by a much simpler model, for a wide variety of cases, and maximum‐likelihood estimates of parameters for this model can be obtained readily by the method‐of‐scoring.

Suggested Citation

  • Byron J. T. Morgan, 1985. "The Cubic Logistic Model for Quantal Assay Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 34(2), pages 105-113, June.
    • Handle: RePEc:bla:jorssc:v:34:y:1985:i:2:p:105-113
    DOI: 10.2307/2347362
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    Cited by:

    1. Huang, Yangxin, 2001. "Interval estimation of the ED50 when a logistic dose-response curve is incorrectly assumed," Computational Statistics & Data Analysis, Elsevier, vol. 36(4), pages 525-537, June.
    2. Yangxin Huang, 2002. "Robustness of interval estimation of the 90% effective dose: Bootstrap resampling and some large-sample parametric methods," Journal of Applied Statistics, Taylor & Francis Journals, vol. 29(7), pages 1071-1081.
    3. Hanemann, W. Michael & Kanninen, Barbara, 1996. "The Statistical Analysis Of Discrete-Response Cv Data," CUDARE Working Papers 25022, University of California, Berkeley, Department of Agricultural and Resource Economics.
    4. Yangxin Huang, 2003. "Selection of number of dose levels and its robustness for binary response data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(10), pages 1135-1146.

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