The Influence of Design Characteristics on Statistical Inference in Nonlinear Estimation: A Simulation Study Based on Survival Data and Hazard Modeling

This paper describes the influence of design characteristics on the statistical inference for an ecotoxicological hazard-based model using simulated survival data. The design characteristics of interest are the number and spacing of observations (counts) in time, the number and spacing of exposure c...

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Bibliographische Detailangaben
Veröffentlicht in:Journal of Agricultural, Biological, and Environmental Statistics. - Springer Science + Business Media. - 5(2000), 3, Seite 323-341
1. Verfasser: Andersen, J. S. (VerfasserIn)
Weitere Verfasser: Bedaux, J. J. M., Holst, H.
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2000
Zugriff auf das übergeordnete Werk:Journal of Agricultural, Biological, and Environmental Statistics
Schlagworte:Large-sample theory No-effect concentration Profile likelihood Small samples Physical sciences Social sciences Mathematics Environmental studies Philosophy Applied sciences Information science
Beschreibung
Zusammenfassung:This paper describes the influence of design characteristics on the statistical inference for an ecotoxicological hazard-based model using simulated survival data. The design characteristics of interest are the number and spacing of observations (counts) in time, the number and spacing of exposure concentrations (within cmin and cmax), and the initial number of individuals at time 0 in each concentration. A comparison of the coverage probabilities for confidence limits arising from the profile-likelihood approach and the Wald-based approach is carried out. The Wald-based approach is very sensitive to the choice of design characteristics, whereas the profile-likelihood approach is more robust and unbiased. Special attention is paid to estimating a parametric no-effect concentration in realistic small-sample situations since this is the most interesting parameter from an environmental protection point of view.
ISSN:15372693
DOI:10.2307/1400457