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Article summary:
1. This article proposes and demonstrates criteria for terminating bootstrap resampling in order to minimize the uncertainty of a bootstrap standard error-based prediction interval.
2. The article reviews the distinctions between model-based and design-based inference, and discusses the use of nonlinear regression models, non-parametric techniques, and machine learning prediction techniques for estimating population means.
3. The article presents seven international forest inventory datasets used to develop a procedure that specifies criteria for terminating resampling that assure in probability that the bootstrap estimate of the standard error stabilizes to the estimate obtained with one million replications.
Article analysis:
This article provides an overview of model-based inference and its application to seven international forest inventory datasets. The authors present a procedure for terminating bootstrap resampling such that it assures in probability that the bootstrap estimate of the standard error stabilizes to the estimate obtained with one million replications.
The article is generally well written and provides a comprehensive overview of model-based inference and its application to forest inventories. However, there are some potential biases worth noting. First, while the authors provide an overview of both model-based and design-based inference, they focus primarily on model-based inference without providing an equal amount of detail on design-based inference. This could lead readers to believe that model-based inference is superior or more reliable than design-based inference when this may not be true in all cases.
Second, while the authors provide an overview of various prediction techniques such as nonlinear regression models, nonparametric techniques, and machine learning prediction techniques, they do not discuss any potential risks associated with these methods or how they might be improved upon in future research. This could lead readers to believe that these methods are infallible when this may not be true in all cases.
Finally, while the authors provide a detailed description of their proposed procedure for terminating bootstrap resampling, they do not discuss any potential limitations or drawbacks associated with this method or how it might be improved upon in future research. This could lead readers to believe that this method is foolproof when this may not be true in all cases.
In conclusion, this article provides a comprehensive overview of model-based inference and its application to seven international forest inventory datasets but does not adequately address potential biases or risks associated with these methods or how they might be improved upon in future research.