Journal ArticleDOI
Design of Experiment for Bioassay
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In this article, the use of two prior distributions, one for design and another for inference, is discussed and graphs are given for designing experiments when the prior distributions are normal, showing the importance of using additional dose levels when the variance of the prior distribution is large.Abstract:
The one-parameter logistic distribution is used to illustrate certain numerical approximations for finding one- and two-stage bioassay designs which produce small posterior variances. The article discusses the use of two prior distributions, one for design and another for inference. Graphs are given for designing experiments when the prior distributions are normal. These graphs illustrate the importance of using additional dose levels when the variance of the prior distribution is large.read more
Citations
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References
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Journal ArticleDOI
The Up-and-Down Method with Small Samples
TL;DR: The up-and-down method for quantal data was originally devised for testing the sensitivity of explosives as mentioned in this paper, and it has been shown to be 30 or 40 per cent more efficient than the usual probit analysis method.
Journal ArticleDOI
Maximum Likelihood and Minimum x 2 Estimates of the Logistic Function
TL;DR: In this paper, the Mayo Foundation defined the minimum x' estimate, which is the classic x' of Pearson, as a function of the expectation PN of the Pearson distribution.
The Use of Prior Probability Distributions in Statistical Inference and Decisions
TL;DR: In this article, the authors discuss some of the general points that arise in common statistical problems when a prior probability distribution is used and explore the consequences of using such a prior distribution.
Journal ArticleDOI
Optimal Bayesian sequential estimation of the median effective dose
TL;DR: In this paper, the median lethal dose parameter of a quantal, logistic dose response curve is estimated using Bayesian decision theory and a stopping rule and terminal decision rule to minimize the prior expectation of the total cost of observation plus estimation loss.