Estimation of the intensity function of an inhomogeneous Poisson process with a change‐point
Copyright policy )Estimation of the intensity function of an inhomogeneous Poisson process with a change‐point
AbstractRecent work on point processes includes studying posterior convergence rates of estimating a continuous intensity function. In this article, convergence rates for estimating the intensity function and change‐point are derived for the more general case of a piecewise continuous intensity function. We study the problem of estimating the intensity function of an inhomogeneous Poisson process with a change‐point using non‐parametric Bayesian methods. An Markov Chain Monte Carlo (MCMC) algorithm is proposed to obtain estimates of the intensity function and the change‐point which is illustrated using simulation studies and applications. The Canadian Journal of Statistics 47: 604–618; 2019 © 2019 Statistical Society of Canada
- University of Wollongong Australia
- University College Dublin Ireland
MCMC, Markov processes: estimation; hidden Markov models, Poisson process, Science and Technology Studies, Bayesian consistency, Engineering, Asymptotic properties of nonparametric inference, Point processes (e.g., Poisson, Cox, Hawkes processes), Nonparametric estimation, Gaussian process
MCMC, Markov processes: estimation; hidden Markov models, Poisson process, Science and Technology Studies, Bayesian consistency, Engineering, Asymptotic properties of nonparametric inference, Point processes (e.g., Poisson, Cox, Hawkes processes), Nonparametric estimation, Gaussian process
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