Spatio-temporal point processes, partial likelihood, foot and mouth disease
Spatio-temporal point processes, partial likelihood, foot and mouth disease
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DOI：
10.1191/0962280206sm454oa
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发表时间：
2006-01-01
影响因子：
2.3
通讯作者：
Diggle, Peter J.
中科院分区：
文献类型：
--
作者：
Diggle, Peter J.
时空点过程数据出现在许多应用领域。指定时空点过程模型的一种直观自然的方法是通过其在位置 x 和时间 t 处的条件强度（给定直到时间 t 的过程历史记录）。通常，这会导致分析上难以处理的可能性。然后，基于似然的推理依赖于蒙特卡洛方法，该方法计算量大，并且需要对每个应用程序进行仔细调整。提出了部分似然替代方案，其计算简单并且可以常规应用。该方法适用于 2001 年英国口蹄疫疫情的数据，使用之前发布的疾病时空传播模型。
Spatio-temporal point process data arise in many fields of application. An intuitively natural way to specify a model for a spatio-temporal point process is through its conditional intensity at location x and time t, given the history of the process up to time t. Often, this results in an analytically intractable likelihood. Likelihood-based inference then relies on Monte Carlo methods which are computationally intensive and require careful tuning to each application. A partial likelihood alternative is proposed, which is computationally straightforward and can be applied routinely. The method is applied to data from the 2001 foot and mouth epidemic in the UK, using a previously published model for the spatio-temporal spread of the disease.