Statistical Inference for Piecewise-deterministic Markov Processes (Mathematics and Statistics)

Piecewise-deterministic Markov processes form a class of stochastic models with a sizeable scope of applications: biology, insurance, neuroscience, networks, finance... Such processes are defined by a deterministic motion punctuated by random jumps at random times, and offer simple yet challenging models to study. Nevertheless, the issue of statistical estimation of the parameters ruling the jump mechanism is far from trivial.

Responding to new developments in the field as well as to current research interests and needs, Statistical inference for piecewise-deterministic Markov processes offers a detailed and comprehensive survey of state-of-the-art results. It covers a wide range of general processes as well as applied models. The present book also dwells on statistics in the context of Markov chains, since piecewise-deterministic Markov processes are characterized by an embedded Markov chain corresponding to the position of the process right after the jumps.

Romain Azaïs is a researcher in applied mathematics. He completed his PhD thesis on nonparametric statistics for piecewise-deterministic Markov processes in Bordeaux in 2013. After a postdoctoral position in Montpellier, he obtained a permanent research position at Inria Nancy. He moved to ENS Lyon in 2018.

Florian Bouguet is currently a CPGE teaching professor of mathematics in China. He completed his PhD thesis on piecewise-deterministic Markov models at Rennes University, France, in 2016.