IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v113y2016icp16-22.html
   My bibliography  Save this article

A weighted simulation-based estimator for incomplete longitudinal data models

Author

Listed:
  • Li, Daniel H.
  • Wang, Liqun

Abstract

Recently, Li and Wang (2012a,b) and Wang (2007) have proposed a simulation-based estimator for generalized linear and nonlinear mixed models with complete longitudinal data. This estimator is constructed using the simulation-by-parts technique which leads to the unique feature that it is consistent even using finite number of simulated random points. This paper extends the methodology to deal with incomplete longitudinal data by applying the inverse probability weighting method for the monotone missing-at-random response data. The finite sample performance of this estimator is investigated through simulation studies and compared with the multiple imputation approach.

Suggested Citation

  • Li, Daniel H. & Wang, Liqun, 2016. "A weighted simulation-based estimator for incomplete longitudinal data models," Statistics & Probability Letters, Elsevier, vol. 113(C), pages 16-22.
    • Handle: RePEc:eee:stapro:v:113:y:2016:i:c:p:16-22
    DOI: 10.1016/j.spl.2016.02.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S016771521530273X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.spl.2016.02.004?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. James R. Carpenter & Michael G. Kenward & Stijn Vansteelandt, 2006. "A comparison of multiple imputation and doubly robust estimation for analyses with missing data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(3), pages 571-584, July.
    2. Horton N. J. & Lipsitz S. R., 2001. "Multiple Imputation in Practice: Comparison of Software Packages for Regression Models With Missing Variables," The American Statistician, American Statistical Association, vol. 55, pages 244-254, August.
    3. Vonesh E. F. & Wang H. & Nie L. & Majumdar D., 2002. "Conditional Second-Order Generalized Estimating Equations for Generalized Linear and Nonlinear Mixed-Effects Models," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 271-283, March.
    4. Shaun R. Seaman & Ian R. White & Andrew J. Copas & Leah Li, 2012. "Combining Multiple Imputation and Inverse-Probability Weighting," Biometrics, The International Biometric Society, vol. 68(1), pages 129-137, March.
    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. Iván Díaz & Nima S. Hejazi, 2020. "Causal mediation analysis for stochastic interventions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(3), pages 661-683, July.
    2. Ashkan Ertefaie & Nima S. Hejazi & Mark J. van der Laan, 2023. "Nonparametric inverse‐probability‐weighted estimators based on the highly adaptive lasso," Biometrics, The International Biometric Society, vol. 79(2), pages 1029-1041, June.
    3. Duo Qin & Sophie van Huellen & Raghda Elshafie & Yimeng Liu & Thanos Moraitis, 2019. "A Principled Approach to Assessing Missing-Wage Induced Selection Bias," Working Papers 216, Department of Economics, SOAS University of London, UK.
    4. Creemers, An & Aerts, Marc & Hens, Niel & Molenberghs, Geert, 2012. "A nonparametric approach to weighted estimating equations for regression analysis with missing covariates," Computational Statistics & Data Analysis, Elsevier, vol. 56(1), pages 100-113, January.
    5. Susanne Rässler & Regina T. Riphahn, 2006. "Survey Item Nonresponse and its Treatment," Springer Books, in: Olaf Hübler & Jachim Frohn (ed.), Modern Econometric Analysis, chapter 15, pages 215-230, Springer.
    6. Ferrari, Pier Alda & Annoni, Paola & Barbiero, Alessandro & Manzi, Giancarlo, 2011. "An imputation method for categorical variables with application to nonlinear principal component analysis," Computational Statistics & Data Analysis, Elsevier, vol. 55(7), pages 2410-2420, July.
    7. Y. T. Hwang & C. H. Huang & W. L. Yeh & Y. D. Shen, 2017. "The weighted general linear model for longitudinal medical cost data – an application in colorectal cancer," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(2), pages 288-307, January.
    8. Janet MacNeil Vroomen & Iris Eekhout & Marcel G. Dijkgraaf & Hein van Hout & Sophia E. de Rooij & Martijn W. Heymans & Judith E. Bosmans, 2016. "Multiple imputation strategies for zero-inflated cost data in economic evaluations: which method works best?," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 17(8), pages 939-950, November.
    9. Aderiana Mutheu Mbandi & Jan R. Böhnke & Dietrich Schwela & Harry Vallack & Mike R. Ashmore & Lisa Emberson, 2019. "Estimating On-Road Vehicle Fuel Economy in Africa: A Case Study Based on an Urban Transport Survey in Nairobi, Kenya," Energies, MDPI, vol. 12(6), pages 1-28, March.
    10. Calzolari, Giorgio & Neri, Laura, 2002. "Imputation of continuous variables missing at random using the method of simulated scores," MPRA Paper 22986, University Library of Munich, Germany, revised 2002.
    11. Kheradmandi, Ameneh & Rasekh, Abdolrahman, 2015. "Estimation in skew-normal linear mixed measurement error models," Journal of Multivariate Analysis, Elsevier, vol. 136(C), pages 1-11.
    12. Hildegard Seidl & Matthias Hunger & Reiner Leidl & Christa Meisinger & Rupert Wende & Bernhard Kuch & Rolf Holle, 2015. "Cost-effectiveness of nurse-based case management versus usual care for elderly patients with myocardial infarction: results from the KORINNA study," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 16(6), pages 671-681, July.
    13. Christian Seiler, 2013. "Nonresponse in Business Tendency Surveys: Theoretical Discourse and Empirical Evidence," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 52.
    14. Youngwon Nam & Cäzilia Loibl, 2021. "Financial Capability and Financial Planning at the Verge of Retirement Age," Journal of Family and Economic Issues, Springer, vol. 42(1), pages 133-150, March.
    15. Ahmad R. Alsaber & Jiazhu Pan & Adeeba Al-Hurban, 2021. "Handling Complex Missing Data Using Random Forest Approach for an Air Quality Monitoring Dataset: A Case Study of Kuwait Environmental Data (2012 to 2018)," IJERPH, MDPI, vol. 18(3), pages 1-25, February.
    16. Daniel, Rhian M. & Kenward, Michael G., 2012. "A method for increasing the robustness of multiple imputation," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1624-1643.
    17. Kristian Kleinke & Mark Stemmler & Jost Reinecke & Friedrich Lösel, 2011. "Efficient ways to impute incomplete panel data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(4), pages 351-373, December.
    18. Geronimi, J. & Saporta, G., 2017. "Variable selection for multiply-imputed data with penalized generalized estimating equations," Computational Statistics & Data Analysis, Elsevier, vol. 110(C), pages 103-114.
    19. Goerke, Laszlo & Pannenberg, Markus, 2015. "Trade union membership and sickness absence: Evidence from a sick pay reform," Labour Economics, Elsevier, vol. 33(C), pages 13-25.
    20. Nengsih Titin Agustin & Bertrand Frédéric & Maumy-Bertrand Myriam & Meyer Nicolas, 2019. "Determining the number of components in PLS regression on incomplete data set," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 18(6), pages 1-28, December.

    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:eee:stapro:v:113:y:2016:i:c:p:16-22. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    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.