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On asymptotic normality and variance estimation for nondifferentiable survey estimators

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  • Jianqiang C. Wang
  • J. D. Opsomer

Abstract

Survey estimators of population quantities such as distribution functions and quantiles contain nondifferentiable functions of estimated quantities. The theoretical properties of such estimators are substantially more complicated to derive than those of differentiable estimators. In this article, we provide a unified framework for obtaining the asymptotic design-based properties of two common types of nondifferentiable estimators. Estimators of the first type have an explicit expression, while those of the second are defined only as the solution to estimating equations. We propose both analytical and replication-based design-consistent variance estimators for both cases, based on kernel regression. The practical behaviour of the variance estimators is demonstrated in a simulation experiment. Copyright 2011, Oxford University Press.

Suggested Citation

  • Jianqiang C. Wang & J. D. Opsomer, 2011. "On asymptotic normality and variance estimation for nondifferentiable survey estimators," Biometrika, Biometrika Trust, vol. 98(1), pages 91-106.
    • Handle: RePEc:oup:biomet:v:98:y:2011:i:1:p:91-106
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    File URL: http://hdl.handle.net/10.1093/biomet/asq077
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    Citations

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    Cited by:

    1. Raphael André Fraser & Stuart R. Lipsitz & Debajyoti Sinha & Garrett M. Fitzmaurice & Yi Pan, 2016. "Approximate median regression for complex survey data with skewed response," Biometrics, The International Biometric Society, vol. 72(4), pages 1336-1347, December.
    2. J. A. Mayor-Gallego & J. L. Moreno-Rebollo & M. D. Jiménez-Gamero, 2019. "Estimation of the finite population distribution function using a global penalized calibration method," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 103(1), pages 1-35, March.
    3. Domingo Morales & María del Mar Rueda & Dolores Esteban, 2018. "Model-Assisted Estimation of Small Area Poverty Measures: An Application within the Valencia Region in Spain," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 138(3), pages 873-900, August.
    4. Zhao, Puying & Haziza, David & Wu, Changbao, 2020. "Survey weighted estimating equation inference with nuisance functionals," Journal of Econometrics, Elsevier, vol. 216(2), pages 516-536.
    5. Francesco Schirripa Spagnolo & Nicola Salvati & Antonella D’Agostino & Ides Nicaise, 2020. "The use of sampling weights in M‐quantile random‐effects regression: an application to Programme for International Student Assessment mathematics scores," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(4), pages 991-1012, August.
    6. Omer Ozturk & Narayanaswamy Balakrishnan & Olena Kravchuk, 2023. "Order Statistics Based on a Combined Simple Random Sample from a Finite Population and Applications to Inference," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 77-101, February.
    7. Pier Luigi Conti & Daniela Marella, 2015. "Inference for Quantiles of a Finite Population: Asymptotic versus Resampling Results," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(2), pages 545-561, June.
    8. Antonio Arcos & José M. Contreras & María M. Rueda, 2014. "A Novel Calibration Estimator in Social Surveys," Sociological Methods & Research, , vol. 43(3), pages 465-489, August.

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