This book covers an extensive class of models involving inhomogeneous Poisson processesand deals with their identification, i.e. the solution of certain estimation or hypothesistesting problems based on the given dataset. These processes are mathematically easy-to-handle and appear in numerous disciplines, including astronomy, biology, ecology, geology,seismology, medicine, physics, statistical mechanics, economics, image processing, forestry,telecommunications, insurance and finance, reliability, queuing theory, wireless networks,and localisation of sources.Beginning with the definitions and properties of some fundamental notions (stochasticintegral, likelihood ratio, limit theorems, etc.), the book goes on to analyse a wide class ofestimators for regular and singular statistical models. Special attention is paid to problemsof change-point type, and in particular cusp-type change-point models, then the focus turnsto the asymptotically efficient nonparametric estimation of the mean function, the intensityfunction, and of some functionals. Traditional hypothesis testing, including some goodness-of-fit tests, is also discussed.The theory is then applied to three classes of problems: misspecification in regularity (MiR),corresponding to situations where the chosen change-point model and that of the real datahave different regularity; optical communication with phase and frequency modulation ofperiodic intensity functions; and localization of a radioactive (Poisson) source on the planeusing K detectors.Each chapter concludes with a series of problems, and state-of-the-art references areprovided, making the book invaluable to researchers and students working in areas whichactively use inhomogeneous Poisson processes.weiterlesen