IDEAS home Printed from https://ideas.repec.org/a/spr/stmapp/v31y2022i4d10.1007_s10260-021-00615-0.html
   My bibliography  Save this article

Model-assisted calibration with SCAD to estimated control for non-probability samples

Author

Listed:
  • Zhan Liu

    (Hubei University)

  • Chaofeng Tu

    (Hubei University)

  • Yingli Pan

    (Hubei University)

Abstract

Non-probability samples have been used in various fields in recent years. However, they usually can result in biased estimates. Calibration to estimated control has been proposed to reduce bias from non-probability samples. The relationship models between the study variable and covariates will help to improve the efficiency of calibration. Specifically, the selection of important covariates is a key issue in establishing the relationship models. In this paper, model-assisted calibration to estimated control using the smoothly clipped absolute deviation (SCAD) is proposed to make inference from non-probability samples. Instead of the traditional chi-square distance, the modified forward Kullback–Leibler distance is explored in the proposed method and the corresponding asymptotic properties are derived. Moreover, the classical variable selection approach SCAD is also implemented to conduct both variable selection and parameter estimation in establishing the relationship models for calibration. The performances of the proposed method are investigated through simulation studies, and an application to analyze a non-probability sample from the National Health Interview Survey in 2017.

Suggested Citation

  • Zhan Liu & Chaofeng Tu & Yingli Pan, 2022. "Model-assisted calibration with SCAD to estimated control for non-probability samples," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(4), pages 849-879, October.
  • Handle: RePEc:spr:stmapp:v:31:y:2022:i:4:d:10.1007_s10260-021-00615-0
    DOI: 10.1007/s10260-021-00615-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10260-021-00615-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10260-021-00615-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wu C. & Sitter R. R, 2001. "A Model-Calibration Approach to Using Complete Auxiliary Information From Survey Data," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 185-193, March.
    2. Montanari, Giorgio E. & Ranalli, M. Giovanna, 2005. "Nonparametric Model Calibration Estimation in Survey Sampling," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1429-1442, December.
    3. Z. Tan, 2013. "Simple design-efficient calibration estimators for rejective and high-entropy sampling," Biometrika, Biometrika Trust, vol. 100(2), pages 399-415.
    4. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    5. Changbao Wu, 2003. "Optimal calibration estimators in survey sampling," Biometrika, Biometrika Trust, vol. 90(4), pages 937-951, December.
    6. Éric Lesage & David Haziza & Xavier D’Haultfœuille, 2019. "A Cautionary Tale on Instrumental Calibration for the Treatment of Nonignorable Unit Nonresponse in Surveys," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 906-915, April.
    7. M. Rueda & I. Sánchez-Borrego & A. Arcos & S. Martínez, 2010. "Model-calibration estimation of the distribution function using nonparametric regression," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 71(1), pages 33-44, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Barranco-Chamorro, I. & Jiménez-Gamero, M.D. & Moreno-Rebollo, J.L. & Muñoz-Pichardo, J.M., 2012. "Case-deletion type diagnostics for calibration estimators in survey sampling," Computational Statistics & Data Analysis, Elsevier, vol. 56(7), pages 2219-2236.
    2. Changbao Wu & Shixiao Zhang, 2019. "Comments on: Deville and Särndal’s calibration: revisiting a 25 years old successful optimization problem," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(4), pages 1082-1086, December.
    3. Maria del Mar Rueda, 2019. "Comments on: Deville and Särndal’s calibration: revisiting a 25 years old successful optimization problem," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(4), pages 1077-1081, December.
    4. Denis Devaud & Yves Tillé, 2019. "Rejoinder on: Deville and Särndal’s calibration: revisiting a 25-year-old successful optimization problem," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(4), pages 1087-1091, December.
    5. Changbao Wu & Wilson W. Lu, 2016. "Calibration Weighting Methods for Complex Surveys," International Statistical Review, International Statistical Institute, vol. 84(1), pages 79-98, April.
    6. 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.
    7. M. Rueda & I. Sánchez-Borrego & A. Arcos & S. Martínez, 2010. "Model-calibration estimation of the distribution function using nonparametric regression," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 71(1), pages 33-44, January.
    8. Denis Devaud & Yves Tillé, 2019. "Deville and Särndal’s calibration: revisiting a 25-years-old successful optimization problem," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(4), pages 1033-1065, December.
    9. Jan Pablo Burgard & Ralf Münnich & Martin Rupp, 2019. "A Generalized Calibration Approach Ensuring Coherent Estimates with Small Area Constraints," Research Papers in Economics 2019-10, University of Trier, Department of Economics.
    10. Xiaojun Mao & Zhonglei Wang & Shu Yang, 2023. "Matrix completion under complex survey sampling," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(3), pages 463-492, June.
    11. Stearns, Matthew & Singh, Sarjinder, 2008. "On the estimation of the general parameter," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4253-4271, May.
    12. 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.
    13. Aylin Alkaya & H. Öztaş Ayhan & Alptekin Esin, 2017. "Sequential Data Weighting Procedures For Combined Ratio Estimators In Complex Sample Surveys," Statistics in Transition New Series, Polish Statistical Association, vol. 18(2), pages 247-270, June.
    14. Sumanta Adhya & Tathagata Banerjee & Gaurangadeb Chattopadhyay, 2012. "Inference on finite population categorical response: nonparametric regression-based predictive approach," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(1), pages 69-98, January.
    15. Alkaya Aylin & Ayhan H. Öztaş & Esin Alptekin, 2017. "Sequential Data Weighting Procedures for Combined Ratio Estimators in Complex Sample Surveys," Statistics in Transition New Series, Polish Statistical Association, vol. 18(2), pages 247-270, June.
    16. Luis Castro-Martín & María del Mar Rueda & Ramón Ferri-García & César Hernando-Tamayo, 2021. "On the Use of Gradient Boosting Methods to Improve the Estimation with Data Obtained with Self-Selection Procedures," Mathematics, MDPI, vol. 9(23), pages 1-23, November.
    17. Ying Sheng & Yifei Sun & Chiung‐Yu Huang & Mi‐Ok Kim, 2022. "Synthesizing external aggregated information in the presence of population heterogeneity: A penalized empirical likelihood approach," Biometrics, The International Biometric Society, vol. 78(2), pages 679-690, June.
    18. María del Mar Rueda & Sergio Martínez-Puertas & Luis Castro-Martín, 2022. "Methods to Counter Self-Selection Bias in Estimations of the Distribution Function and Quantiles," Mathematics, MDPI, vol. 10(24), pages 1-19, December.
    19. Jason P. Estes & Bhramar Mukherjee & Jeremy M. G. Taylor, 2018. "Empirical Bayes Estimation and Prediction Using Summary-Level Information From External Big Data Sources Adjusting for Violations of Transportability," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(3), pages 568-586, December.
    20. Tan, Zhiqiang, 2014. "Second-order asymptotic theory for calibration estimators in sampling and missing-data problems," Journal of Multivariate Analysis, Elsevier, vol. 131(C), pages 240-253.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:stmapp:v:31:y:2022:i:4:d:10.1007_s10260-021-00615-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.