Volume 20, Issue 1 (6-2021)                   JIRSS 2021, 20(1): 1-26 | Back to browse issues page

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Ahmed S E, Aydın D, Yılmaz E. Kernel Ridge Estimator for the Partially Linear Model under Right-Censored Data. JIRSS. 2021; 20 (1) :1-26
URL: http://jirss.irstat.ir/article-1-774-en.html
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 , sahmed5@brocku.ca
Abstract:   (1768 Views)

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.

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Type of Study: Special Issue, Original Paper | Subject: 62Jxx: Linear inference, regression
Received: 2020/12/8 | Accepted: 2021/01/29 | Published: 2021/06/20

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