神经元放电序列计数模型的拟合优度检验
On Goodness of Fit Tests For Models of Neuronal Spike Trains Considered as Counting Processes
摘要：
在对“时间变换”进行初等推导后，将计数过程映射到速率为1的齐次Poisson过程上，简要回顾了Ogata的拟合优度检验，并提出了一种新的检验方法--“Wiener过程检验”。这个测试是基于一个简单的应用Donsker定理的时间转换计数过程的间隔。用蒙特卡罗模拟研究了检验的有限样本性质。模拟以及真实数据上的性能。它认为，由于其良好的有限样本性质，新的测试是一个简单的和有用的补充绪方检验。此外，对使用一个单一的拟合优度检验给出了。
英文摘要：
After an elementary derivation of the "time transformation", mapping a counting process onto a homogeneous Poisson process with rate one, a brief review of Ogata's goodness of fit tests is presented and a new test, the "Wiener process test", is proposed. This test is based on a straightforward application of Donsker's Theorem to the intervals of time transformed counting processes. The finite sample properties of the test are studied by Monte Carlo simulations. Performances on simulated as well as on real data are presented. It is argued that due to its good finite sample properties, the new test is both a simple and a useful complement to Ogata's tests. Warnings are moreover given against the use of a single goodness of fit test.
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