Journal of the Iranian Statistical Society

Kernel Ridge Estimator for the Partially Linear Model under Right-Censored Data

Authors

1 Brock University, Faculty of Mathematics and Science, Department of Mathematics and Statistics, Niagara Region, 1812 Sir Isaac Brock Way, St. Catharines, ON, L2S 3A1, Canada

2 Mugla Sitki Kocman University, Faculty of Science, Department of Statistics, 48000, Mugla, Turkey

10.52547/jirss.20.1.1

Abstract

Objective: This paper aims to introduce a modified kernel-type ridge estimator for partially linear models under randomly-right censored data. Such models include two main issues that need to be solved: multi-collinearity and censorship. To address these issues, we improved the kernel estimator based on synthetic data transformation and kNN imputation techniques. The key idea of this paper is to obtain a satisfactory estimate of the partially linear model with multi-collinear and right-censored using a modified ridge estimator. Results: To determine the performance of the method, a detailed simulation study is carried out and a kernel-type ridge estimator for PLM is investigated for two censorship solution techniques. The results are compared and presented with tables and figures. Necessary derivations for the modified semiparametric estimator are given in appendices.

Keywords

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Journal of the Iranian Statistical Society
Volume 20, Issue 1
June 2021
Pages 1-26
  • Receive Date: 23 July 2022
  • Revise Date: 20 May 2024
  • Accept Date: 23 July 2022