@article{Kott:329779,
      recid = {329779},
      author = {Kott, Phillip S.},
      title = {On Calibration Weighting},
      address = {2003},
      number = {1496-2022-2162},
      pages = {41},
      year = {2003},
      note = {An earlier version for this paper was prepared for the  Joint Statistical Meetings, August 2003, San Francisco,  California.},
      abstract = {Calibration weighting is a methodology under which  probability-sample weights are adjusted in such a way that  when applied to survey data they can produce model-unbiased  estimators for a number of different target variables.   This paper briefly reviews the history of calibration  weighting before the term was coined and some major  developments since then.  A change in the definition of a  calibration estimator is recommended.  This change expands  the class to include such special cases as, 1,  randomization-optimal estimators, and, 2,  randomization-consistent estimators incorporating local  polynomial regression.  Although originally developed as a  method for reducing sampling errors, calibration weighting  has also been applied to adjust for unit nonresponse and  for coverage errors.  A variant of the jackknife variance  estimator proposed here should prove computationally  convenient for these applications.},
      url = {http://ageconsearch.umn.edu/record/329779},
      doi = {https://doi.org/10.22004/ag.econ.329779},
}