This paper considers the estimation of a nonparametric regression model with randomly right-censored data. To estimate the model, rational (Padé) approximation based on truncated total least squares (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 (B-KS) method to observe how P-TTLS behaves.
Pade approximation right-censored data kernel smoothing truncated total least squares
Birincil Dil | İngilizce |
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Bölüm | Makaleler |
Yazarlar | |
Yayımlanma Tarihi | 27 Kasım 2020 |
Yayımlandığı Sayı | Yıl 2020 Cilt: 21 |