Introduces computationally simple M-estimators for trend and seasonality in temporal or spatial doubly-stochastic Poisson point processes with log-Gaussian intensities, derives their asymptotic distributions, and evaluates them via simulations and Chicago bike-sharing data.
(2010).Poisson Point Processes: Imaging, Tracking, and Sensing.Springer, New York
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Trend and seasonality estimation for point-process time series
Introduces computationally simple M-estimators for trend and seasonality in temporal or spatial doubly-stochastic Poisson point processes with log-Gaussian intensities, derives their asymptotic distributions, and evaluates them via simulations and Chicago bike-sharing data.