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Weather modelling using a multivariate latent Gaussian model

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dc.affiliation.dptoUC3M. Departamento de Estadísticaes
dc.contributor.authorDurbán, María
dc.contributor.authorGlasbey, C.A.
dc.date.accessioned2006-11-08T15:55:54Z
dc.date.available2006-11-08T15:55:54Z
dc.date.issued2001-03
dc.description.abstractWe propose a vector autoregressive moving average process as a model for daily weather data. For the rainfall variable a monotonic transformation is applied to achieve marginal normality, thus defining a latent variable, with zero rainfall data corresponding to censored values below a threshold. Methodology is presented for model identification, estimation and validation, illustrated using data from Mynefield, Scotland. The new model, a VARMA(2,1) process, fits the data and produces more realistic simulated series than existing methods dur to Richardson (1981) and Peiris and McNicol (1996).
dc.format.extent339621 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.repecws011610
dc.identifier.urihttp://hdl.handle.net/10016/157
dc.language.isoeng
dc.relation.ispartofseriesUC3M Working Papers. Statistics and Econometrics
dc.relation.ispartofseries2001-10
dc.rights.accessRightsopen access
dc.subject.ecienciaEstadística
dc.titleWeather modelling using a multivariate latent Gaussian model
dc.typeworking paper*
dspace.entity.typePublication
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