TY - JOUR
T1 - Small area estimation of general parameters under complex sampling designs.
AU - Guadarrama, María
AU - Molina, Isabel
AU - Rao, J.N.K.
PY - 2018/5/1
Y1 - 2018/5/1
N2 - When the probabilities of selecting individuals (units) for the sample depend on the outcome values, the selection mechanism is said to be informative. Under informative selection, individuals with certain outcome values appear more often in the sample and, as a consequence, usual inference based on the actual sample without appropriate weighting might be strongly biased. An extension of the empirical best (EB) method for estimation of general non-linear parameters in small areas that handles informative selection by incorporating the sampling weights is proposed. Properties of this new method under complex sampling designs, including informative selection, are analyzed. Results confirm that the proposed weighted estimators significantly reduce the bias of unweighted EB estimators under informative sampling, and compare favorably under non-informative sampling. The proposed method is illustrated through an application to poverty mapping in a State from Mexico.
AB - When the probabilities of selecting individuals (units) for the sample depend on the outcome values, the selection mechanism is said to be informative. Under informative selection, individuals with certain outcome values appear more often in the sample and, as a consequence, usual inference based on the actual sample without appropriate weighting might be strongly biased. An extension of the empirical best (EB) method for estimation of general non-linear parameters in small areas that handles informative selection by incorporating the sampling weights is proposed. Properties of this new method under complex sampling designs, including informative selection, are analyzed. Results confirm that the proposed weighted estimators significantly reduce the bias of unweighted EB estimators under informative sampling, and compare favorably under non-informative sampling. The proposed method is illustrated through an application to poverty mapping in a State from Mexico.
KW - Empirical best estimator
KW - Informative sampling
KW - Nested-error model
KW - Poverty mapping
KW - Unit level models
U2 - 10.1016/j.csda.2017.11.007
DO - 10.1016/j.csda.2017.11.007
M3 - Article
SN - 0167-9473
VL - 121
SP - 20
EP - 40
JO - Computational Statistics and Data Analysis
JF - Computational Statistics and Data Analysis
ER -