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Nonparametric additive model-assisted estimation for survey data

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  • Wang, Li
  • Wang, Suojin

Abstract

An additive model-assisted nonparametric method is investigated to estimate the finite population totals of massive survey data with the aid of auxiliary information. A class of estimators is proposed to improve the precision of the well known Horvitz-Thompson estimators by combining the spline and local polynomial smoothing methods. These estimators are calibrated, asymptotically design-unbiased, consistent, normal and robust in the sense of asymptotically attaining the Godambe-Joshi lower bound to the anticipated variance. A consistent model selection procedure is further developed to select the significant auxiliary variables. The proposed method is sufficiently fast to analyze large survey data of high dimension within seconds. The performance of the proposed method is assessed empirically via simulation studies.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:jmvana:v:102:y:2011:i:7:p:1126-1140
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    References listed on IDEAS

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    1. Opsomer, Jean D. & Breidt, F. Jay & Moisen, Gretchen G. & Kauermann, Goran, 2007. "Model-Assisted Estimation of Forest Resources With Generalized Additive Models," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 400-409, June.
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    5. Li Wang, 2009. "Single-index model-assisted estimation in survey sampling," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 21(4), pages 487-504.
    6. Sperlich, Stefan & Tjøstheim, Dag & Yang, Lijian, 2002. "Nonparametric Estimation And Testing Of Interaction In Additive Models," Econometric Theory, Cambridge University Press, vol. 18(2), pages 197-251, April.
    7. F. J. Breidt & G. Claeskens & J. D. Opsomer, 2005. "Model-assisted estimation for complex surveys using penalised splines," Biometrika, Biometrika Trust, vol. 92(4), pages 831-846, December.
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    1. Xu, Bin & Lin, Boqiang, 2015. "How industrialization and urbanization process impacts on CO2 emissions in China: Evidence from nonparametric additive regression models," Energy Economics, Elsevier, vol. 48(C), pages 188-202.
    2. Wang, Li & Wang, Suojin & Wang, Guannan, 2014. "Variable selection and estimation for longitudinal survey data," Journal of Multivariate Analysis, Elsevier, vol. 130(C), pages 409-424.

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