Modelling the effects of partially observed covariates on Poisson process intensity. (English) Zbl 1142.62391
Summary: We propose an estimating function for parameters in a model for Poisson process intensity when time- or space-varying covariates are observed for both the events of the process and at sample times or locations selected from a probability-based sampling design. We investigate the large-sample properties of the proposed estimator under increasing domain asymptotics, demonstrating that it is consistent and asymptotically normally distributed. We illustrate our approach using data from an ecological momentary assessment of smoking.
MSC:
62M05 | Markov processes: estimation; hidden Markov models |
62P12 | Applications of statistics to environmental and related topics |
62F12 | Asymptotic properties of parametric estimators |