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Comparison of Robust Bayes and Classical Estimators for Regional Lake Models of Fish Response to Acidification

机译：区域湖泊鱼类酸化响应模型的鲁棒Bayes和经典估计的比较

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Empirical models of fish response to lake acidification were recently fit to a large historical data set from the Adirondack region of the United States using classical and Bayesian methods. The models may be used to predict species presence/absence for brook trout and lake trout as a function of acid-precipitation-related water chemistry, using a logistic function. To evaluate the effectiveness of the models in the prediction of presence/absence due to regional lake acidification new data sets were used for cross validation of the candidate models. Based on the evaluation, the robust Bayes models, which are based on a compromise estimator between Bayes and empirical Bayes, were found to be the best predictors of species presence/absence in lakes.

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