"Rescaling, thinning or complementing? On goodness-of-ﬁt procedures for point process models and Generalized Linear Models" by Gerhard and Gerstner (NIPS 2010).
The abstract reads:
"Generalized Linear Models (GLMs) are an increasingly popular framework for modeling neural spike trains. They have been linked to the theory of stochastic point processes and researchers have used this relation to assess goodness-of-ﬁt using methods from point-process theory, e.g. the time-rescaling theorem. However, high neural ﬁring rates or coarse discretization lead to a breakdown of the assumptions necessary for this connection. Here, we show how goodness-of-ﬁt tests from point-process theory can still be applied to GLMs by constructing equivalent surrogate point processes out of time-series observations. Furthermore, two additional tests based on thinning and complementing point processes are introduced. They augment the instruments available for checking model adequacy of point processes as well as discretized models."