0366043 20240103195724.120120120235959.9 10.1007/978-3-642-20853-9_22 14 s. 512 cav_un_epca*0371888978-3-642-20852-21860-0832315-328BerlinSpringer2011PardoL.BalakrishnanN.GilM. A. small area estimation random regression coefficient model EBLUP estimates cav_un_auth*0101108 Hobza Tomáš Stochastická informatika Department of Stochastic Informatics SI SI UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0015540 Morales D. ES http://library.utia.cas.cz/separaty/2011/SI/hobza-small area estimation of poverty proportions under random regression coefficient models.pdf 1M0572 GA MŠk cav_un_auth*0001814 CEZ:AV0Z10750506 In this paper a random regression coefficient model is used to provide estimates of small area poverty proportions. As poverty variable is dichotomic at the individual level, the sample data from Spanish Living Conditions Survey is previously aggregated to the level of census sections. EBLUP estimates based on the proposed model are obtained. A closed-formula procedure to estimate the mean squared error of the EBLUP estimators is given and empirically studied. Results of several simulations studies are reported as well as an application to real data. 2012 BB 2 http://hdl.handle.net/11104/0201140 2011 cav_un_epca*0371888 Modern Mathematical Tools and Techniques in Capturing Complexity 978-3-642-20852-2 1860-0832 315 328 Berlin Springer 2011 Understanding Complex Systems Springer Complexity Pardo L. 340 Balakrishnan N. 340 Gil M. A. 340