Vol.5, No.4, November 2016.                                                                                                              ISSN: 2217-8309

                                                                                                                                                           eISSN: 2217-8333


TEM Journal



Association for Information Communication Technology Education and Science

Right-Censored Nonparametric Regression: A Comparative Simulation Study


Dursun Aydın, Ersin Yılmaz


© 2016 Dursun Aydın, Ersin Yılmaz, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)


Citation Information: TEM Journal. Volume 5, Issue 4, Pages 446-450, ISSN 2217-8309, DOI: 10.18421/TEM54-06, November 2016.




This paper introduces the operating of the selection criteria for right-censored nonparametric regression using smoothing spline. In order to transform the response variable into a variable that contains the right-censorship, we used the KaplanMeier weights proposed by [1], and [2]. The major problem in smoothing spline method is to determine a smoothing parameter to obtain nonparametric estimates of the regression function. In this study, the mentioned parameter is chosen based on censored data by means of the criteria such as improved Akaike information criterion (AICc), Bayesian (or Schwarz) information criterion (BIC) and generalized crossvalidation (GCV). For this purpose, a Monte-Carlo simulation study is carried out to illustrate which selection criterion gives the best estimation for censored data.


Keywords – Nonparametric Regression, Spline Smoothing, Kaplan-Meier weights, Censored data.



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