Open to: Academic | Student
Admission: Free
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Speaker information

Enno Mammen, Heidelberg

A flexible class of time-varying link-based continuous-time random network models based on counting processes is considered, and a rigorous analysis of corresponding maximum likelihood estimators is presented. The model parameters are allowed to be functions of time. The presented asymptotic analysis assumes the time horizon tending to infinity. Our model allows for both Markovian and non-Markovian structures. We present results on asymptotic normality of corresponding local maximum likelihood estimators, and illustrate the finite sample performance of the estimation procedure through numerical studies. The talk reports on joint work with Alexander Kreiss, Heidelberg and Wolfgang Polonik, Davis.

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Seminar paper