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Problem
To simplify the computation of the variance in before-after studies, it is generally assumed that the observed crash data for each entity (or observation) are Poisson distributed. Given the characteristics of this distribution, the observed value (xi) for each entity is implicitly made equal to its variance. However, the variance should be estimated using the conditional properties of this observed value (defined as a random variable), that is, f(xi|μi), since the mean of the observed value is in fact unknown.Method
Parametric and non-parametric bootstrap methods were investigated to evaluate the conditional assumption using simulated and observed data.Results
The results of this study show that observed data should not be used as a substitute for the variance, even if the entities are assumed to be Poisson distributed. Consequently, the estimated variance for the parameters under study in traditional before-after studies is likely to be underestimated.Conclusions
The proposed methods offer more accurate approaches for estimating the variance in before-after studies. 相似文献2.
Brooke E. Buckley Walter W. Piegorsch R. Webster West 《Environmental and Ecological Statistics》2009,16(1):53-62
In modern environmental risk analysis, inferences are often desired on those low dose levels at which a fixed benchmark risk
is achieved. In this paper, we study the use of confidence limits on parameters from a simple one-stage model of risk historically
popular in benchmark analysis with quantal data. Based on these confidence bounds, we present methods for deriving upper confidence
limits on extra risk and lower bounds on the benchmark dose. The methods are seen to extend automatically to the case where
simultaneous inferences are desired at multiple doses. Monte Carlo evaluations explore characteristics of the parameter estimates
and the confidence limits under this setting.
相似文献
R. Webster WestEmail: |
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Predictive models of wildlife-habitat relationships often have been developed without being tested The apparent classification accuracy of such models can be optimistically biased and misleading. Data resampling methods exist that yield a more realistic estimate of model classification accuracy These methods are simple and require no new sample data. We illustrate these methods (cross-validation, jackknife resampling, and bootstrap resampling) with computer simulation to demonstrate the increase in precision of the estimate. The bootstrap method is then applied to field data as a technique for model comparison We recommend that biologists use some resampling procedure to evaluate wildlife habitat models prior to field evaluation. 相似文献
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