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A design‐based approach to small area estimation using a semiparametric generalized linear mixed model

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  • Hongjian Yu
  • Yueyan Wang
  • Jean Opsomer
  • Pan Wang
  • Ninez A. Ponce

Abstract

In small area estimation, non‐parametric models with penalized spline regression have been demonstrated to be a useful tool in creating granular area estimates to provide supplemental information where samples are few or non‐existent. This study further examines the ability of a semiparametric generalized linear mixed model to produce conforming estimates for multiple area levels. A mosaic analogy is used to describe this process. A design‐based jackknife method is employed for variance calculation.

Suggested Citation

  • Hongjian Yu & Yueyan Wang & Jean Opsomer & Pan Wang & Ninez A. Ponce, 2018. "A design‐based approach to small area estimation using a semiparametric generalized linear mixed model," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(4), pages 1151-1167, October.
    • Handle: RePEc:bla:jorssa:v:181:y:2018:i:4:p:1151-1167
    DOI: 10.1111/rssa.12351
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    References listed on IDEAS

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    1. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167, January.
    2. Yu, H. & Meng, Y.-Y. & Mendez-Luck, C.A. & Jhawar, M. & Wallace, S.P., 2007. "Small-area estimation of health insurance coverage for California legislative districts," American Journal of Public Health, American Public Health Association, vol. 97(4), pages 731-737.
    3. Marchetti, Stefano & Tzavidis, Nikos & Pratesi, Monica, 2012. "Non-parametric bootstrap mean squared error estimation for M-quantile estimators of small area averages, quantiles and poverty indicators," Computational Statistics & Data Analysis, Elsevier, vol. 56(10), pages 2889-2902.
    4. Nicholas T. Longford & Maria Grazia Pittau & Roberto Zelli & Riccardo Massari, 2012. "Poverty and inequality in European regions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(7), pages 1557-1576, January.
    5. Wang, Y. & Ponce, N.A. & Wang, P. & Opsomer, J.D. & Yu, H., 2015. "Generating health estimates by zip code: A semiparametric small area estimation approach using the California health interview survey," American Journal of Public Health, American Public Health Association, vol. 105(12), pages 2534-2540.
    6. Malay Ghosh & Rebecca Steorts, 2013. "Two-stage benchmarking as applied to small area estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(4), pages 670-687, November.
    7. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506, January.
    8. J. D. Opsomer & G. Claeskens & M. G. Ranalli & G. Kauermann & F. J. Breidt, 2008. "Non‐parametric small area estimation using penalized spline regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 265-286, February.
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