Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/197778
Authors: 
Espuny-Pujol, Ferran
Morrissey, Karyn
Williamson, Paul
Year of Publication: 
2016
Series/Report no.: 
HEG Working Paper 16-01
Abstract: 
Survey calibration methods modify minimally unit-level sample weights to fit domain-level benchmark constraints (BC). This allows exploitation of auxiliary information, e.g. census totals, to improve the representativeness of sample data (addressing coverage limitations, non-response) and the quality of estimates of population parameters. Calibration methods may fail with samples presenting small/zero counts for some benchmark groups or when range restrictions (RR), such as positivity, are imposed to avoid unrealistic or extreme weights. User-defined modifications of BC/RR performed after encountering non-convergence allow little control on the solution, and penalization approaches modelling infeasibility may not guarantee convergence. Paradoxically, this has led to underuse in calibration of highly disaggregated information, when available. We present an always-convergent flexible two-step Global Optimisation (GO) survey calibration approach. The feasibility of the calibration problem is assessed, and automatically controlled minimum errors in BC or changes in RR are allowed to guarantee convergence in advance, while preserving the good properties of calibration estimators. Modelling alternatives under different scenarios, using various error/change and distance measures are formulated and discussed. The GO approach is validated by calibrating the weights of the 2012 Health Survey for England to a fine age/gender/region cross-tabulation (378 counts) from the 2011 Census in England and Wales.
Subjects: 
Calibration estimation
Calibration weighting
Design-based inference
Generalised regression
Penalized calibration
Raking
Ridge calibration
Range restrictions
Survey weighting
Document Type: 
Working Paper

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