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Imputation Method Based on Sliding Window for Right-Censored Data

  • Syed Ejaz Ahmed
  • Dursun Aydın
  • Ersin YılmazEmail author
Conference paper
  • 20 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1190)

Abstract

Censored data arise in almost all important statistical analyses. For example, in patient-based studies, biostatistics data often subject to right censoring due to the detection limits, or to incomplete data. In the context of regression analysis, improper handling of these problems may lead to biased parameter estimates. Recently, imputation techniques are popularly used to impute censoring observations and the data are then analyzed through techniques that can handle censoring. In this sense, we introduce a new imputation strategy called sliding window method based on predictive model imputation (SWPM). In the present study, to assess the success of the proposed imputation method, the classical predictive model (PM) is used as a benchmark method. Hence, we compared two imputation methods for evaluating the right-censored data. The focus here is to assess and analyze through simulation and real data studies the performances of our imputation techniques based on different censoring levels and sample sizes.

Keywords

Sliding window Imputation Predictive model imputation Censored data 

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Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Syed Ejaz Ahmed
    • 1
  • Dursun Aydın
    • 2
  • Ersin Yılmaz
    • 2
    Email author
  1. 1.Department of Mathematics and StatisticsBrock UniversitySt. CatharinesCanada
  2. 2.Department of StatisticsMugla Sitki Kocman UniversityMuglaTurkey

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