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Title: Model-assisted estimators in surveys with nonresponse Authors:  Caren Hasler - University of Neuchatel (Switzerland) [presenting]
Esther Eustache - University of Neuchatel (Switzerland)
Abstract: In the presence of auxiliary information, model-assisted estimators use a working model that links the variable of interest and the auxiliary variables in order to improve the Horvitz-Thompson estimator. The resulting estimators are asymptotically designed unbiased and asymptotically more efficient than the Horvitz-Thompson estimator under some regularity conditions and for a wide range of working models. We adapt model-assisted total estimators to missing at random data building on the idea of nonresponse weighting adjustment. We see nonresponse as a second phase of the survey and reweight the units in model-assisted estimators using the inverse of estimated response probabilities in order to compensate for the nonrespondents. We develop the asymptotic properties of our proposed estimators and discuss the calibration of the weights of these estimators. We provide formulae for asymptotic variance and variance estimators. We conduct a simulation study to describe the behavior of the proposed estimators.