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Hidden Markov Models incorporating additional dependence in regional rainfall modelling

Hidden Markov Models incorporating additional dependence in regional rainfall modelling

Ramesh, Nadarajah (2011) Hidden Markov Models incorporating additional dependence in regional rainfall modelling. In: 2011 Joint Statistical Meetings (JSM2011), July 30–August 4, 2011, Miami Beach, Florida, USA.

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Abstract

Hidden Markov models can be modified in several ways to form a rich class of flexible models that are useful in many environmental applications. One of the issues that come up very often when basic hidden Markov models are used to model environmental data is about their ability to accommodate sufficient dependence between observations. We consider some class of hidden Markov models that incorporate additional dependence among observations to model daily rainfall time series. The focus of the study is on models that introduce additional dependence between the state level and the observation level of the process and also on models that incorporate dependence at observation level. Construction of the likelihood function of the models is described along with the usual second order properties of the process. Maximum likelihood method is used to estimate the parameters of the models. Application of the proposed class of models is illustrated in an analysis of regional daily rainfall time series from South East England during 1931 to 2010.

Item Type: Conference or Conference Paper (Paper)
Uncontrolled Keywords: Hidden Markov models, Rainfall modelling, Maximum likelihood, Precipitation series
Subjects: G Geography. Anthropology. Recreation > GE Environmental Sciences
Faculty / Department / Research Group: Faculty of Architecture, Computing & Humanities
Faculty of Architecture, Computing & Humanities > Department of Mathematical Sciences
Last Modified: 10 Nov 2017 14:14
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/17981

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