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Single-index model-assisted estimation in survey sampling

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  • Li Wang

Abstract

A model-assisted semiparametric method of estimating finite-population totals is investigated to improve the precision of survey estimators by incorporating multivariate auxiliary information. The proposed superpopulation model is a single-index model (SIM) which has proven to be a simple and efficient semiparametric tool in multivariate regression. A class of estimators based on polynomial spline regression is proposed. These estimators are robust against deviation from SIMs. Under standard design conditions, the proposed estimators are asymptotically design-unbiased, consistent and asymptotically normal. An iterative optimisation routine is provided that is sufficiently fast for users to analyze large and complex survey data within seconds. The proposed method has been applied to simulated datasets and MU281 dataset, which have provided strong evidence that corroborates with the asymptotic theory.

Suggested Citation

  • Li Wang, 2009. "Single-index model-assisted estimation in survey sampling," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 21(4), pages 487-504.
    • Handle: RePEc:taf:gnstxx:v:21:y:2009:i:4:p:487-504
    DOI: 10.1080/10485250902773849
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    Cited by:

    1. Wang, Li & Wang, Suojin, 2011. "Nonparametric additive model-assisted estimation for survey data," Journal of Multivariate Analysis, Elsevier, vol. 102(7), pages 1126-1140, August.
    2. Lan Xue & Jing Wang, 2010. "Distribution function estimation by constrained polynomial spline regression," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 22(4), pages 443-457.

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