A Bayesian mixture model for missing data in marine mammal growth analysis.
A Bayesian mixture model for missing data in marine mammal growth analysis.
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DOI：
10.1007/s10651-016-0355-x
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发表时间：
2016-12
影响因子：
3.8
通讯作者：
Slate EH
中科院分区：
文献类型：
--
作者：
Shotwell ME;McFee WE;Slate EH
关于瓶鼻海豚(Tursioptruncatus)的解剖学和生理学知识，大部分都是基于搁浅事件的身体解剖。测量总体长、总体重和年龄是用来估计生长的。回收和运输较小的动物用于总体重测量比较大的动物更可行，这在抽样中引入了系统性的偏差。不利的天气事件、志愿者的可获得性和其他不可预见的情况也会导致测量不完整。我们开发了一个贝叶斯混合模型来描述检测到的搁浅动物的生长，使用了来自完全测量和未完全测量的数据。我们的方法使用共享的随机效应将缺失机制(即完全/部分测量)与完全和部分测量的总体中不同的增长曲线联系起来，从而能够为估计提供力量。通过模拟比较我们的模型完成了案例分析，并根据模型的均方误差完成了两种常见的多重归责方法。结果表明，我们的混合模型在两个种群存在和不存在时都提供了更好的拟合。通过对南卡罗来纳州绞线数据的应用，验证了新方法的可行性和实用性。
Much of what is known about bottle nose dolphin (Tursiops truncatus) anatomy and physiology is based on necropsies from stranding events. Measurements of total body length, total body mass, and age are used to estimate growth. It is more feasible to retrieve and transport smaller animals for total body mass measurement than larger animals, introducing a systematic bias in sampling. Adverse weather events, volunteer availability, and other unforeseen circumstances also contribute to incomplete measurement. We have developed a Bayesian mixture model to describe growth in detected stranded animals using data from both those that are fully measured and those not fully measured. Our approach uses a shared random effect to link the missingness mechanism (i.e. full/partial measurement) to distinct growth curves in the fully and partially measured populations, thereby enabling drawing of strength for estimation. We use simulation to compare our model to complete case analysis and two common multiple imputation methods according to model mean square error. Results indicate that our mixture model provides better fit both when the two populations are present and when they are not. The feasibility and utility of our new method is demonstrated by application to South Carolina strandings data.