RecordNumber
3940
Author
Ahmed, Syed Ejaz
Crop_Body
Syed Ejaz Ahmed, Dursun Aydın, and Ersin Yılmaz
Title of Article
Kernel Ridge Estimator for the Partially Linear Model under Right-Censored Data
Title Of Journal
خبرنامه انجمن آمار ايران (JIRSS)
PublishInfo
تهران :انجمن آمار ايران
Publication Year
2021
Volum
20
Issue Number
1
Page
1-26
Keywords
Kernel Smoothing , KNNImputation , Multi-Collinear Data , Partially Linear Model , Ridge Type Estimator , Right-Censored Data
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.