Right-Censored Time Series Modeling by Modified Semi-Parametric A-Spline Estimator

• Published in: Entropy (Basel, Switzerland) Switzerland), 2021-11, Vol.23 (12), p.1586
• Main Authors: Aydın, Dursun, Ahmed, Syed Ejaz, Yılmaz, Ersin
• Format: Article
• Language: English
• Subjects: adaptive splines; B-splines; Censorship; Datasets; Methods; Monte Carlo simulation; Random variables; Regression analysis; Regression models; right-censored data; semiparametric regression; Synthetic data; synthetic data transformation; Time series
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e23121586

This paper focuses on the adaptive spline (A-spline) fitting of the semiparametric regression model to time series data with right-censored observations. Typically, there are two main problems that need to be solved in such a case: dealing with censored data and obtaining a proper A-spline estimator for the components of the semiparametric model. The first problem is traditionally solved by the synthetic data approach based on the Kaplan-Meier estimator. In practice, although the synthetic data technique is one of the most widely used solutions for right-censored observations, the transformed data's structure is distorted, especially for heavily censored datasets, due to the nature of the approach. In this paper, we introduced a modified semiparametric estimator based on the A-spline approach to overcome data irregularity with minimum information loss and to resolve the second problem described above. In addition, the semiparametric B-spline estimator was used as a benchmark method to gauge the success of the A-spline estimator. To this end, a detailed Monte Carlo simulation study and a real data sample were carried out to evaluate the performance of the proposed estimator and to make a practical comparison.