477 – Calibration Estimation and Weighting in Sample Surveys
Calibrated Maximum Likelihood Design Weights in Survey Sampling
Stephen A. Sedory
Texas A&M University at Kingsville
Sarjinder Singh
Texas A&M University at Kingsville
In this paper, we propose a new technique to calibrate the design weights in survey sampling by the method of maximum likelihood. We show that the design weights used in the Narain (1951) and the Horvitz and Thompson (1952) estimators are in fact maximum likelihood design weights. Later, we discuss two different situations: ( a ) when the variance of the calibrated weights is assumed to be known; and ( b ) when the variance of the calibrated weights is assumed to be unknown. Under situation ( a ), we obtain the linear regression estimator as a special case of it, and under situation ( b ) we obtain a new estimator, slightly different than the linear regression estimator. The calibrated estimators available since Deville and Srndal (1992) belong to the former case ( a ) whereas case ( b ) is a new development in this area. A simulation study has been carried out to investigate the performance of the resultant estimators. At the end, an application based on a real dataset from the biosciences is given.
