The Effective Use of Complete Auxiliary Information From Surveys
         Through Model Calibration and Empirical Likelihood

                          Changbao Wu 

             Department of Mathematics and Statistics
                Simon Fraser University, CANADA


   Complete auxiliary information is often available from survey data.
By complete we mean the values of auxiliary variable(s) are known for
the entire finite population, not just in the sample. One of the
fundamental questions is how to effectively use the complete auxiliary
information at the estimation stage. In this work, a unified framework
has been attempted under a general modeling process. The proposed model
calibration estimators of the population mean and total can effectively 
handle any linear or non-linear models and reduce to the generalized 
regression estimators under linear models. The newly proposed pseudo 
empirical likelihood estimator (Chen and Sitter, 1999), when used in 
this setting, gives an estimator that is asymptotically equivalent to 
the model calibration estimator but with positive weights, and therefore 
is prefered. Some existing estimators using auxiliary information are
re-examined and some related issues, in particular, the estimation of the 
finite  population distribution function and quantiles, are discussed 
under the framework. Results of a limited simulation study are reported.