Adapted <fc>GLM</fc> gamma parameter estimates for drop size distributions

Dan Brawn

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Adapted GLM gamma parameter estimates for drop size distributions

机译：为液滴尺寸分布调整了 GLM 伽玛参数估计

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This article outlines an alternative maximum likelihood gamma parameter estimator for grouped and truncated data. Such data might be produced by traditional disdrometer instruments for recording rain drop sizes. The method is an adaptation of a log‐linear generalized linear model ( GLM ), forming an iterative process which quickly converges to very near maximum likelihood estimates. Simulations with a Joss–Waldvogel bin regime show that the estimator has little bias. Application to observed data from two different types of disdrometers yields results practically equal to published maximum likelihood estimates.

机译：本文概述了用于分组和截断数据的替代最大似然伽玛参数估计器。此类数据可能由传统的测速仪产生，用于记录雨滴大小。该方法是对数线性广义线性模型（GLM）的改编，形成了一个迭代过程，该过程迅速收敛到非常接近的最大似然估计。用Joss-Waldvogel bin方案进行的模拟表明，估计量几乎没有偏差。将两种不同类型的测速仪应用于观测数据，得出的结果实际上等于已公布的最大似然估计。

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《Atmospheric science letters》 |2015年第3期|共5页
作者
Dan Brawn;
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中图分类大气科学（气象学）;
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Adapted GLM gamma parameter estimates for drop size distributions

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