Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
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)
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)
Burgette, L.F., Reiter, J.P.: Multiple imputation for missing data via sequential regression trees. Am. J. Epidemiol. 172(9), 1070–1076 (2010)
Cai, Z., Heydari, M., Lin, G.: Iterated local least squares microarray missing value imputation. J. Bioinf. Comput. Biol. 4(05), 935–957 (2006)
Cheng, R., Chen, J., Xie, X.: Cleaning uncertain data with quality guarantees. Proc. VLDB Endow. 1(1), 722–735 (2008)
Crowley, J., Hu, M.: Covariance analysis of heart transplant survival data. J. Am. Stat. Assoc. 72(357), 27–36 (1977)
Kaplan, E.L., Meier, P.: Nonparametric estimation from incomplete observations. J. Am. Stat. Assoc. 53(282), 457–481 (1958)
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)
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)
Koul, H., Vv, S., Van Ryzin, J., et al.: Regression analysis with randomly right-censored data. Ann. Stat. 9(6), 1276–1288 (1981)
Miller, R.G.: Least squares regression with censored data. Biometrika 63(3), 449–464 (1976)
Pipino, L.L., Lee, Y.W., Wang, R.Y.: Data quality assessment. Commun. ACM 45(4), 211–218 (2002)
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix
Appendix
Rights and permissions
Copyright information
© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Ahmed, S.E., Aydın, D., Yılmaz, E. (2020). Imputation Method Based on Sliding Window for Right-Censored Data. In: Xu, J., Duca, G., Ahmed, S., García Márquez, F., Hajiyev, A. (eds) Proceedings of the Fourteenth International Conference on Management Science and Engineering Management. ICMSEM 2020. Advances in Intelligent Systems and Computing, vol 1190. Springer, Cham. https://doi.org/10.1007/978-3-030-49829-0_32
Download citation
DOI: https://doi.org/10.1007/978-3-030-49829-0_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-49828-3
Online ISBN: 978-3-030-49829-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)