<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sartore, L.</style></author><author><style face="normal" font="default" size="100%">Toppin, K.</style></author><author><style face="normal" font="default" size="100%">Spiegelman, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Estimated Covariance Matrices Associated with Calibration</style></title><secondary-title><style face="normal" font="default" size="100%">JSM 2017</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Agriculture</style></keyword><keyword><style  face="normal" font="default" size="100%">Calibration</style></keyword><keyword><style  face="normal" font="default" size="100%">Census</style></keyword><keyword><style  face="normal" font="default" size="100%">Estimation</style></keyword><keyword><style  face="normal" font="default" size="100%">NASS</style></keyword><keyword><style  face="normal" font="default" size="100%">Survey</style></keyword><keyword><style  face="normal" font="default" size="100%">Variance</style></keyword><keyword><style  face="normal" font="default" size="100%">Weighting</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">Submitted</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.niss.org/sites/default/files/Sartore_Variance_Estim_20170926.pdf</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Surveys often provide numerous estimates of population parameters. Some of the population values&amp;nbsp;may be known to lie within a small range of values with a high level of certainty. Calibration is used&amp;nbsp;to adjust survey weights associated with the observations within a data set. This process ensures&amp;nbsp;that the “sample” estimates for the target population totals (benchmarks) lie within the anticipated&amp;nbsp;ranges of those population values. The additional uncertainty due to the calibration process needs&amp;nbsp;to be captured. In this paper, some methods for estimating the variance of the population totals are&amp;nbsp;proposed for an algorithmic calibration process based on minimizing the L1-norm relative error.&amp;nbsp;The estimated covariance matrices for the calibration totals are produced either by linear approximations&amp;nbsp;or bootstrap techniques. Specific data structures are required to allow for the computation&amp;nbsp;of massively large covariance matrices. In particular, the implementation of the proposed algorithms&amp;nbsp;exploits sparse matrices to reduce the computational burden and memory usage. The computational&amp;nbsp;efficiency is shown by a simulation study.&lt;/p&gt;
</style></abstract></record></records></xml>