Article ID: | iaor20122797 |
Volume: | 52 |
Issue: | 3 |
Start Page Number: | 471 |
End Page Number: | 485 |
Publication Date: | Mar 2012 |
Journal: | Journal of Global Optimization |
Authors: | Mnnich Ralf, Sachs Ekkehard, Wagner Matthias |
Keywords: | statistics: inference, programming: nonlinear |
Weighting is a common methodology in survey statistics to increase accuracy of estimates or to compensate for non‐response. One standard approach for weighting is calibration estimation which represents a common numerical problem. There are various approaches in the literature available, but quite a number of distance‐based approaches lack a mathematical justification or are numerically unstable. In this paper we reformulate the calibration problem as a system of nonlinear equations. Although the equations are lacking differentiability properties, one can show that they are semismooth and the corresponding extension of Newton’s method is applicable. This is a mathematically rigorous approach and the numerical results show the applicability of this method.