We propose a class of counting process models for analysing firing times of an ensemble of neurons. We allow the counting process intensities to be unspecified, unknown functions of the times passed since the most recent firings. Under this assumption we derive a class of statistics with their...

From noisy observations of a finite family of functions an approximation in a lower dimensional space can be constructed using the method of principal components. If certain restrictions are to be satisfied by the approximation, e.g. being densities, this leads to a modified estimation...

Consider a p-variate counting process N = (...) with jump times {...}. Suppose that the intensity of jumps ... of ... at time t depends on the other components, i. e. ..., where the ... are unknown, nonrandom functions. From observing one single trajectory of the processes N over an increasing...

Consider a random permutation of a finite number $n$ of known elements representing rewards. These rewards will be made or not made with certain known probabilities. At any stage a reward made can be accepted or rejected, there is no recall and only one reward can be accepted. The problem is to...