@misc{Horabik-Pyzel_Joanna_Methodology_2014,
 author={Horabik-Pyzel, Joanna and Nahorski, Zbigniew (1945– )},
 copyright={Creative Commons Attribution BY 4.0 license},
 address={Warszawa},
 journal={Raport Badawczy = Research Report},
 howpublished={online},
 year={2014},
 publisher={Instytut Badań Systemowych. Polska Akademia Nauk},
 publisher={Systems Research Institute. Polish Academy of Sciences},
 language={eng},
 abstract={This paper presents a novel appmach to allocation of spatially correlated data, such as emission inventories, to finer spatial scales, conditional on covariate information observable in a fine grid. Spatial dependence is modelled with the conditional autoregressive structure int:roduced into a linear model as a random effect The maximum likelihood appmach to inference is employed, and the optimal predictors are developed to as.c;ess missing values in a fine grid. An example of ammonia emission inventory is used to illustrate the potential usefulness of the pmposed technique. The results indicate that incllL'iion of a spatial dependence structure can compensate for less adequate covariate infonnation. For the considered ammonia inventory, the fourfold allocation benefited greatly from incorporation of the spatial component, while for the ninefold allocation this advantage was limited, but stili evident Jn addition, the proposed method allows correction of the prediction bias encountered for the upper range emissions in the linear regression models.},
 title={Methodology for Spatial Scaling of GHG activity data * lmproving resolution of a spatial air pollution inventory with a statistical inference approach},
 type={Text},
 URL={http://rcin.org.pl/Content/208704/PDF/RB-2014-08-02.pdf},
}