Abstract
Censored data is a kind of data type where the exact value of a response variable is not completely known. Therefore, this case is a problem that should be solved in order to obtain an accurate and efficient data analysis. Recently, imputation methods have been used in order to overcome censored data problems, especially in medical research and microarray data sets. In this study, we compared two imputation methods, k-nearest neighbors (kNN) and a prediction model (PM), for the evaluation of right-censored data. In order to see the effects of the imputation methods on the nonparametric regression estimates, the imputed right-censored data modelled by the penalized splines for two methods. We also supported the study with a Monte Carlo simulation experiment and a real data study.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Andridge, R.R., Little, R.J.A.: A review of hot deck imputation for survey non-response. Int. Stat. Rev. 78(1), 40–64 (2010)
Batista, G., Monard, M.: An analysis of four missing data treatment methods for supervised learning. Appl. Artif. Intell. 17(5–6), 519–533 (2003)
Chen, J., Shao, J.: Nearest neighbor imputation for survey data. J. Off. Stat. 16(2), 113 (2000)
Cartwright, M.H., Shepperd, M.J., Song, Q.: Dealing with missing software project data. In: Proceedings of the 9th International Software Metrics Symposium, Sydney, Australia, pp. 154–165 (2003)
Doreswamy, H., Gad, I.M., Manjunatha, B.R.: Performance evaluation of predictive models for missing data imputation in weather data. In: 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (2017)
Malarvizhi, R., Thanamani, A.S.: Framework for missing value imputation. Int. J. Eng. Res. Dev. 4(2012), 14–16 (2012)
Parmer, M., Machin, D.: Survival Analysis. Wiley, UK (1995)
Rubin, D.B.: Multiple Imputation for Nonresponse in Survey. Wiley, NewYork (1987)
Ruppert, D., Wand, M.P., Carroll, R.J.: Semiparametric Regression. Cambridge University Press, New York (2003)
Rubin, D.B., Van der Laan, M.J.: A general methodology for nonparametric regression with censored data. U.C. Berkeley Division of Biostatistics Working Paper Series. Working Paper 194 (2005)
Schafer, J.L.: Analysis of Incomplete Multivariate Data. Chapman & Hall, London (1997)
Strike, K., El Emam, K., Madhavji, N.: Software cost estimation with incomplete data. IEEE Trans. Softw. Eng. 27(2001), 890–908 (2001)
Steiner, S., Zeng, Y., et al.: A study of missing data imputation in predictive modeling of a wood-composite manufacturing process. J. Qual. Technol. 48(2016), 284–296 (2016)
Van Buuren, S., Boshuzien, H.C., Knook, D.L.: Multiple imputation of missing blood pressure covariates in survival anaylsis. Stat. Med. 18(6), 681–694 (1999)
Wahl, S., Boulesteix, A.L., Avan de Wiel, M.: Assessment of predictive performance in incomplete data by combining internal validation and multiple imputation. BMC Med. Res. Methodol. 16(1), 170 (2016)
Yenduri, S., Iyengar, S.S.: Performance evaluation of imputation methods for incomplete datasets. Int. J. Softw. Eng. Knowl. Eng. 17(01), 127–152 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Ahmed, S.E., Aydin, D., Yılmaz, E. (2020). Nonparametric Regression Estimates Based on Imputation Techniques for Right-Censored Data. In: Xu, J., Ahmed, S., Cooke, F., Duca, G. (eds) Proceedings of the Thirteenth International Conference on Management Science and Engineering Management. ICMSEM 2019. Advances in Intelligent Systems and Computing, vol 1001. Springer, Cham. https://doi.org/10.1007/978-3-030-21248-3_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-21248-3_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-21247-6
Online ISBN: 978-3-030-21248-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)