Modern Mathematical Tools and Techniques in Capturing Complexity

0366043 - ÚTIA 2012 RIV DE eng M - Monography Chapter
Hobza, Tomáš - Morales, D.
Small Area Estimation of Poverty Proportions under Random Regression Coefficient Models.
Modern Mathematical Tools and Techniques in Capturing Complexity. Berlin: Springer, 2011 - (Pardo, L.; Balakrishnan, N.; Gil, M.), s. 315-328. Understanding Complex Systems Springer Complexity. ISBN 978-3-642-20852-2
R&D Projects: GA MŠMT 1M0572
Institutional research plan: CEZ:AV0Z10750506
Keywords : small area estimation * random regression coefficient model * EBLUP estimates
Subject RIV: BB - Applied Statistics, Operational Research
http://library.utia.cas.cz/separaty/2011/SI/hobza-small area estimation of poverty proportions under random regression coefficient models.pdf

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.
Permanent Link: http://hdl.handle.net/11104/0201140