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Arxiv.org
by Gianluca Mastrantonio; Gianfranco Calise
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In this work, we deal with a bivariate time series of wind speed and direction. Our observed data have peculiar features, such as informative missing values, non-reliable measures under a specific condition and interval-censored data, that we take into account in the model specification. We analyze the time series with a non-parametric Bayesian hidden Markov model, introducing a new emission distribution based on the invariant wrapped Poisson, the Poisson and the hurdle density, suitable to...
Topics: Statistics, Methodology
Source: http://arxiv.org/abs/1704.05037
Arxiv.org
by Gianluca Mastrantonio; Alessio Pollice; Francesca Fedele
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Winds from the North-West quadrant and lack of precipitation are known to lead to an increase of PM10 concentrations over a residential neighborhood in the city of Taranto (Italy). In 2012 the local government prescribed a reduction of industrial emissions by 10% every time such meteorological conditions are forecasted 72 hours in advance. Wind forecasting is addressed using the Weather Research and Forecasting (WRF) atmospheric simulation system by the Regional Environmental Protection Agency....
Topics: Statistics, Applications
Source: http://arxiv.org/abs/1704.05028
Arxiv.org
by Gianluca Mastrantonio; Giovanna Jona Lasinio; Alan E. Gelfand
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We consider modeling of angular or directional data viewed as a linear variable wrapped onto a unit circle. In particular, we focus on the spatio-temporal context, motivated by a collection of wave directions obtained as computer model output developed dynamically over a collection of spatial locations. We propose a novel wrapped skew Gaussian process which enriches the class of wrapped Gaussian process. The wrapped skew Gaussian process enables more flexible marginal distributions than the...
Topics: Statistics, Methodology
Source: http://arxiv.org/abs/1704.05032
Arxiv.org
by Gianluca Mastrantonio; Giovanna Jona Lasinio; Alan E. Gelfand
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Circular data arise in many areas of application. Recently, there has been interest in looking at circular data collected separately over time and over space. Here, we extend some of this work to the spatio-temporal setting, introducing space-time dependence. We accommodate covariates, implement full kriging and forecasting, and also allow for a nugget which can be time dependent. We work within a Bayesian framework, introducing suitable latent variables to facilitate Markov chain Monte Carlo...
Topics: Statistics, Methodology
Source: http://arxiv.org/abs/1704.05029