Calibration of estimator-weights via semismooth Newton method

被引:0
作者
Ralf T. Münnich
Ekkehard W. Sachs
Matthias Wagner
机构
[1] University of Trier,Forumstat and Department of Economics
[2] University of Trier,Forumstat and Department of Mathematics
[3] University of Trier,Forumstat—Research Center for Regional and Environmental Statistics
来源
Journal of Global Optimization | 2012年 / 52卷
关键词
Semismooth Newton method; Calibration; Convex objective function; General regression estimator; Sample weights;
D O I
暂无
中图分类号
学科分类号
摘要
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.
引用
收藏
页码:471 / 485
页数:14
相关论文
共 29 条
  • [1] Demnati A.(2004)Linearization variance estimators for survey data Surv. Methodol. 30 17-26
  • [2] Rao J.(1992)Calibration estimators in survey sampling J. Am. Stat. Assoc. 87 376-382
  • [3] Deville J.C.(1993)Generalized raking procedures in survey sampling J. Am. Stat. Assoc. 88 1013-1020
  • [4] Särndal C.E.(2006)Survey estimates by calibration on complex auxiliary information Int. Stat. Rev. 74 127-147
  • [5] Deville J.C.(1997)Solution of monotone complementarity problems with locally lipschitzian functions Math. Program. 76 513-532
  • [6] Särndal C.E.(1952)A generalization of sampling without replacement from a finite universe J. Am. Stat. Assoc. 47 663-685
  • [7] Sautory O.(2011)Calibration estimation in survey sampling Int. Stat. Rev. 78 21-39
  • [8] Estevao V.(2006)Using calibration weighting to adjust for nonresponse and coverage errors Surv. Methodol. 32 133-142
  • [9] Särndal C.E.(1977)Semismooth and semiconvex functions in constrained optimization SIAM J. Contr. Optim. 15 957-972
  • [10] Fischer A.(1993)Nonsmooth equations: motivation and algorithms SIAM J. Optim. 3 443-465