@misc{Horabik-Pyzel_Joanna_Improving_2012,
 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={2012},
 publisher={Instytut Badań Systemowych. Polska Akademia Nauk},
 publisher={Systems Research Institute. Polish Academy of Sciences},
 language={eng},
 abstract={This paper presents a novel approach for 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 introduced into a linear model as a random effect. The maximum likelihood approach to inference is employed, and the optimal predictors are developed to assess missing values in a fine grid. An example of ammonia emission inventory is used to illustrate potential usefulness of the proposed technique. The results indicate that inclusion of a spatial dependence structure can compensate for less adequate covariate information.},
 title={Improving resolution of a spatial air pollution inventory with a statistical inference approach (revised)},
 type={Text},
 URL={http://www.rcin.org.pl/Content/109535/PDF/RB-2012-31.pdf},
 keywords={Inwentaryzacje gazów cieplarnianych, Statistical model, Spatial inventory data, Model statystyczny, Dissagregation methods, Metody dezagregacji},
}