摘要翻译：
本文提出了一种利用空气施肥过程中颗粒流测量装置进行试验所获得的通过时间数据的概率模型。讨论了简单线性布尔模型的最大似然流强估计，该模型是在假设每个粒子需要相同的已知通过时间的情况下产生的。M-估计是一种推广模型，其中通过时间表现为一个已知均值分布的随机样本。在这些实验中，广义模型提高了拟合度。通过对多颗粒团块长度的限制，构造了一个总颗粒流的估计量。
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英文标题：
《M-estimation of Boolean models for particle flow experiments》
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作者：
Jason A. Osborne and Tony E. Grift
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最新提交年份：
2007
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分类信息：

一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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英文摘要：
  Probability models are proposed for passage time data collected in experiments with a device designed to measure particle flow during aerial application of fertilizer. Maximum likelihood estimation of flow intensity is reviewed for the simple linear Boolean model, which arises with the assumption that each particle requires the same known passage time. M-estimation is developed for a generalization of the model in which passage times behave as a random sample from a distribution with a known mean. The generalized model improves fit in these experiments. An estimator of total particle flow is constructed by conditioning on lengths of multi-particle clumps.
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PDF链接：
https://arxiv.org/pdf/707.0462