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Markov modulated Poisson process models incorporating covariates for rainfall intensity

Markov modulated Poisson process models incorporating covariates for rainfall intensity

Thayakaran, R. and Ramesh, N.I. (2013) Markov modulated Poisson process models incorporating covariates for rainfall intensity. Water Science & Technology, 67 (8). pp. 1786-1792. ISSN 0273-1223 (doi:https://doi.org/10.2166/wst.2013.056)

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Abstract

Time series of rainfall bucket tip times at the Beaufort Park station, Bracknell, in the UK are modelled by a class of Markov modulated Poisson processes (MMPP) which may be thought of as a generalization of the Poisson process. Our main focus in this paper is to investigate the effects of
including covariate information into the MMPP model framework on statistical properties. In particular, we look at three types of time-varying covariates namely temperature, sea level pressure, and relative humidity that are thought to be affecting the rainfall arrival process. Maximum likelihood estimation is used to obtain the parameter estimates, and likelihood ratio tests are employed in model comparison. Simulated data from the fitted model are used to make statistical inferences about the accumulated rainfall in the discrete time interval. Variability of the daily Poisson arrival rates is studied.

Item Type: Article
Uncontrolled Keywords: covariates, likelihood function, likelihood ratio test, MMPP, rainfall intensity, tip times
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics
Pre-2014 Departments: School of Computing & Mathematical Sciences
Related URLs:
Last Modified: 14 Oct 2016 09:24
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/10057

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