Imputation Method Based on Sliding Window for Right-Censored Data

  • Syed Ejaz Ahmed
  • Dursun Aydın
  • Ersin YılmazEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1190)


Censored data arise in almost all important statistical analyses. For example, in patient-based studies, biostatistics data often subject to right censoring due to the detection limits, or to incomplete data. In the context of regression analysis, improper handling of these problems may lead to biased parameter estimates. Recently, imputation techniques are popularly used to impute censoring observations and the data are then analyzed through techniques that can handle censoring. In this sense, we introduce a new imputation strategy called sliding window method based on predictive model imputation (SWPM). In the present study, to assess the success of the proposed imputation method, the classical predictive model (PM) is used as a benchmark method. Hence, we compared two imputation methods for evaluating the right-censored data. The focus here is to assess and analyze through simulation and real data studies the performances of our imputation techniques based on different censoring levels and sample sizes.


Sliding window Imputation Predictive model imputation Censored data 


  1. 1.
    Ahmed, S.E., Aydin, D., Yılmaz, E.: Nonparametric regression estimates based on imputation techniques for right-censored data. In: International Conference on Management Science and Engineering Management, pp 109–120. Springer (2019)Google Scholar
  2. 2.
    Bertsimas, D., Pawlowski, C., Zhuo, Y.D.: From predictive methods to missing data imputation: an optimization approach. J. Mach. Learn. Res. 18(1), 7133–7171 (2017)MathSciNetzbMATHGoogle Scholar
  3. 3.
    Bø, T.H., Dysvik, B., Jonassen, I.: LSimpute: accurate estimation of missing values in microarray data with least squares methods. Nucleic Acids Res. 32(3), e34 (2004)CrossRefGoogle Scholar
  4. 4.
    Burgette, L.F., Reiter, J.P.: Multiple imputation for missing data via sequential regression trees. Am. J. Epidemiol. 172(9), 1070–1076 (2010)CrossRefGoogle Scholar
  5. 5.
    Cai, Z., Heydari, M., Lin, G.: Iterated local least squares microarray missing value imputation. J. Bioinf. Comput. Biol. 4(05), 935–957 (2006)CrossRefGoogle Scholar
  6. 6.
    Cheng, R., Chen, J., Xie, X.: Cleaning uncertain data with quality guarantees. Proc. VLDB Endow. 1(1), 722–735 (2008)CrossRefGoogle Scholar
  7. 7.
    Crowley, J., Hu, M.: Covariance analysis of heart transplant survival data. J. Am. Stat. Assoc. 72(357), 27–36 (1977)CrossRefGoogle Scholar
  8. 8.
    Kaplan, E.L., Meier, P.: Nonparametric estimation from incomplete observations. J. Am. Stat. Assoc. 53(282), 457–481 (1958)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Keller, S., Korkmaz, G., Orr, M., Schroeder, A., Shipp, S.: The evolution of data quality: understanding the transdisciplinary origins of data quality concepts and approaches. Ann. Rev. Stat. Appl. 4, 85–108 (2017)CrossRefGoogle Scholar
  10. 10.
    Khan, M.E.E., Bouchard, G., Murphy, K.P., Marlin, B.M.: Variational bounds for mixed-data factor analysis. In: Advances in Neural Information Processing Systems, pp. 1108–1116 (2010)Google Scholar
  11. 11.
    Koul, H., Vv, S., Van Ryzin, J., et al.: Regression analysis with randomly right-censored data. Ann. Stat. 9(6), 1276–1288 (1981)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Miller, R.G.: Least squares regression with censored data. Biometrika 63(3), 449–464 (1976)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Pipino, L.L., Lee, Y.W., Wang, R.Y.: Data quality assessment. Commun. ACM 45(4), 211–218 (2002)CrossRefGoogle Scholar
  14. 14.
    Troyanskaya, O., Cantor, M., Sherlock, G., Brown, P., Hastie, T., Tibshirani, R., Botstein, D., Altman, R.B.: Missing value estimation methods for DNA microarrays. Bioinformatics 17(6), 520–525 (2001)CrossRefGoogle Scholar
  15. 15.
    Wang, X., Li, A., Jiang, Z., Feng, H.: Missing value estimation for DNA microarray gene expression data by support vector regression imputation and orthogonal coding scheme. BMC Bioinf. 7(1), 32 (2006)CrossRefGoogle Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Syed Ejaz Ahmed
    • 1
  • Dursun Aydın
    • 2
  • Ersin Yılmaz
    • 2
    Email author
  1. 1.Department of Mathematics and StatisticsBrock UniversitySt. CatharinesCanada
  2. 2.Department of StatisticsMugla Sitki Kocman UniversityMuglaTurkey

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