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Modelling the effects of partially observed covariates on Poisson process intensity

Modelling the effects of partially observed covariates on Poisson process intensity 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biometrika Oxford University Press

Modelling the effects of partially observed covariates on Poisson process intensity

Biometrika , Volume 94 (1) – Feb 28, 2007

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References (25)

Publisher
Oxford University Press
Copyright
Copyright © 2007 Biometrika Trust
Subject
Articles
ISSN
0006-3444
eISSN
1464-3510
DOI
10.1093/biomet/asm009
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

BiometrikaOxford University Press

Published: Feb 28, 2007

Keywords: ecological momentary assessment increasing domain asymptotics intensity function modulated Poisson process space-varying covariate time-varying covariate

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