Penalized Calibration in Survey Sampling: Design-Based Estimation Assisted by Mixed Models
Fabien Guggemos & Yves Tillé
| Résumé |
Calibration techniques in survey sampling, such as generalized
regression estimation (GREG), were formalized in the 1990s to
produce efficient estimators of linear combinations of study
variables, such as totals or means. They implicitly lie on the
assumption of a linear regression model between the variable of
interest and some auxiliary variables in order to yield estimates
with lower variance if the model is true and remaining
approximately design-unbiased even if the model does not hold. We
propose a new class of model-assisted estimators obtained by
releasing a few calibration constraints and replacing them with a
penalty term. This penalization is added to the distance criterion
to minimize. By introducing the concept of penalizedcalibration,
combining usual calibration and this ‘relaxed’ calibration, we are
able to adjust the weight given to the available auxiliary
information. We obtain a more flexible estimation procedure giving
better estimates particularly when the auxiliary information is
overly abundant or not fully appropriate to be completely used.
Such an approach can also be seen as a design-based alternative to
the estimation procedures based on the more general class of
mixedmodels, presenting new prospects in some scopes of application
such as inference on small domains. |
| Mots-clés |
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| Citation | Guggemos, F., & Tillé, Y. (2010). Penalized Calibration in Survey Sampling: Design-Based Estimation Assisted by Mixed Models. Journal of Statistical Planning and Inference, 140(11), 3199-3212. |
| Type | Article de périodique (Anglais) |
| Date de publication | 23-3-2010 |
| Nom du périodique | Journal of Statistical Planning and Inference |
| Volume | 140 |
| Numéro | 11 |
| Pages | 3199-3212 |
| URL | http://www.sciencedirect.com/science/article/pii/S0378375... |
| Liée au projet | Convention Université de Neuchâtel/Office fédéral de la s... |