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  • 1
    Electronic Resource
    Electronic Resource
    Sampling based approach for one-hit and multi-hit models in quantal bioassay (1997)
    Springer
    Statistics and computing 7 (1997), S. 183-192 
    Details
    ISSN: 1573-1375
    Keywords: Abbott's correction ; conditional predictive ordinate ; low dose extrapolation ; Markov chain Monte Carlo ; Metropolis algorithm ; risk analysis
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract Bayesian methods for estimating the dose response curves with the one-hit model, the gamma multi-hit model, and their modified versions with Abbott's correction are studied. The Gibbs sampling approach with data augmentation and with the Metropolis algorithm is employed to compute the Bayes estimates of the potency curves. In addition, estimation of the ‘relative additional risk’ and the ‘virtually safe dose’ is studied. Model selection based on conditional predictive ordinates from cross-validated data is developed.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1018586118882
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    Paper (German National Licenses)
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  • 2
    Electronic Resource
    Electronic Resource
    A Bayesian predictive approach to determining the number of components in a mixture distribution (1995)
    Springer
    Statistics and computing 5 (1995), S. 297-305 
    Details
    ISSN: 1573-1375
    Keywords: Bootstrap procedures ; conditional predictive ordinate ; gamma mixtures ; Gibbs sampler ; likelihood ratio (LR) statistic ; Metropolis algorithm ; Monte Carlo methods ; normal mixtures ; predictive distribution ; pseudo Bayes factor
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract This paper describes a Bayesian approach to mixture modelling and a method based on predictive distribution to determine the number of components in the mixtures. The implementation is done through the use of the Gibbs sampler. The method is described through the mixtures of normal and gamma distributions. Analysis is presented in one simulated and one real data example. The Bayesian results are then compared with the likelihood approach for the two examples.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00162502
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    Paper (German National Licenses)
    Fulltext
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