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Spatial Bayesian Model for Maximum Temperature

Bisono, Indriati Njoto and Robinson, Andrew P. (2014) Spatial Bayesian Model for Maximum Temperature. International Journal of Applied Mathematics and Statistics, 53 (6). pp. 137-144. ISSN 0973-7545

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    Abstract

    A three-stage Bayesian spatial model is fitted to temperature extremes covering Tasmania. In the first stage, the data in each grid cell are assumed to follow a GEV distribution with particular parameters. In the second stage, each GEV parameter is assumed to follow a Normal distribution with mean structure comprising a fixed and random effect component. A usual regression model with covariates longitude, latitude and elevation is employed for the fixed effect component, and a conditional auto regressive (CAR) model is used for the random effect. The estimation of the posterior parameters was conducted by Monte Carlo method using a hybrid MCMC of Metropolis and Gibbs sampler algorithm. We found the spatial random effect successfully smoothed the shape parameters, so that credible intervals of return levels were well behaved.

    Item Type: Article
    Uncontrolled Keywords: Bayesian, spatial, hierarchical model; CAR; extreme values.
    Subjects: H Social Sciences > HA Statistics
    Divisions: Faculty of Industrial Technology > Industrial Engineering Department
    Depositing User: Admin
    Date Deposited: 26 Jul 2015 03:37
    Last Modified: 28 Jul 2015 17:02
    URI: http://repository.petra.ac.id/id/eprint/17110

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