NONPARAMETRIC REGRESSION ANALYSIS BASED ON RATIONAL (Padé) APPROXIMATION FOR CENSORED-DATA

Authors : Dursun AYDIN, Ersin YILMAZ

Pages : 7-16

Doi:10.18038/estubtda.818004

View : 41 | Download : 12

Publication Date : 2020-11-27

Article Type : Research Paper

Abstract :This paper considers the estimation of a nonparametric regression model with randomly right-censored data. To estimate the model, rational insert ignore into journalissuearticles values(Padé); approximation based on truncated total least squares insert ignore into journalissuearticles values(P-TTLS); is used as a smoothing method. Because of censored, data points cannot be used directly in modeling process, a data transformation is needed for overcoming this problem. As known, synthetic data transformation assigns censored points as zero and gives additional magnitudes to uncensored ones associated with Kaplan-Meier distribution of the censored dataset. Thus, the differences between censored and uncensored observations grow which causes a kind of spatial variation in the shape of data. In this paper, to bring a solution to this problematic situation, P-TTLS is used that works well on spatial variation. Also, to see the performance of the P-TTLS on censored data modeling, a simulation study is carried out and it is compared with the benchmarked kernel smoothing insert ignore into journalissuearticles values(B-KS); method to observe how P-TTLS behaves.
Keywords : Pade approximation, right censored data, kernel smoothing, truncated total least squares

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