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首页> 外文期刊>Journal of applied statistics >Modeling of paired zero-inflated continuous data without breaking down paired designs
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Modeling of paired zero-inflated continuous data without breaking down paired designs

机译：配对零膨胀连续数据的建模而不会破坏配对设计

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摘要

Paired data have been widely collected in the efficiency studies of a new method against an established method in environmental, ecological and medical studies. For example, in comparative fishing studies, ability of catching target species (fish catch) or reducing the catch of non-target species (fish bycatch) is usually investigated through a paired design. These paired fish catches by weight are generally skewed and continuous, but with a significant portion of exact zeros (no catch). Such zero-inflated continuous data are traditionally handled by two-part models where the zero and positive components are handled separately; however, this separation generally destroys paired structure, and thus may result in substantial difficulty in characterizing the relative efficiency between two methods. To overcome this problem, we consider compound Poisson mixed model for paired data with which the zero and non-zero components are characterized in an integral way. In our approach, the clustering effects by pair are captured by incorporating relevant random effects. Our model is estimated using orthodox best linear unbiased predictor approach. Unlike two-part models, our approach unifies inferences of zero and positive components. Our method is illustrated with analyses of winter flounder bycatch data and ultrasound safety data.
机译：在针对环境，生态和医学研究中已建立的方法的新方法的效率研究中，已经广泛收集了配对数据。例如，在比较捕鱼研究中，通常通过配对设计来研究捕获目标物种（鱼获）或减少非目标物种的捕获量（鱼副渔获）的能力。这些成对的按重量计的渔获量通常是偏斜且连续的，但有相当一部分为零（无渔获量）。传统上，这种零膨胀连续数据由两部分模型处理，其中零分量和正分量分开处理；然而，这种分离通常破坏配对的结构，因此可能导致表征两种方法之间的相对效率相当困难。为了克服这个问题，我们考虑了配对数据的复合Poisson混合模型，其中零和非零分量以整体方式表征。在我们的方法中，通过合并相关的随机效应来捕获成对的聚类效应。我们的模型是使用正统最佳线性无偏预测器方法估算的。与两部分模型不同，我们的方法统一了零分量和正分量的推论。我们的方法通过分析冬季比目鱼兼捕数据和超声安全数据来说明。

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