Abstract
A new regression model that considers random nature is proposed for describing summer air temperatures in urban areas through the concept of an empirical hierarchical Bayesian model. In this model, the regression coefficients of explanatory variables are regarded as having random effects because of their locations, whereas ordinal regression models do not allow such randomness. Data of air temperature, the climatic situation, and geographically related factors in the Kinki area were collected and compiled by GIS, and then applied to both the new model and the standard regression models. The performances of the models were compared and their differences were discussed in relation to the geographic or climatic situations. It was established that the proposed model succeeded in accounting for the random nature and it predicted air temperature more accurately than the standard models based on the MCMC simulation. Furthermore, the confidence intervals of the values predicted by the new model became more rigid than for the ordinal regression models. This means that the new model is capable of presenting a more accurate picture without affecting the convenience of the ordinary regression models.
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