Right-censored partially linear regression model with error in variables: application with carotid endarterectomy dataset

Aydın Dursun (), Yılmaz Ersin (), Chamidah Nur () and Lestari Budi ()
Additional contact information
Aydın Dursun: Department of Statistics, Faculty of Science, Mugla Sitki Kocman University, Mugla, Türkiye
Yılmaz Ersin: Department of Statistics, Faculty of Science, Mugla Sitki Kocman University, Mugla, Türkiye
Chamidah Nur: Department of Mathematics, Faculty of Science and Technology, Airlangaa University, Surabaya, Indonesia
Lestari Budi: Department of Mathematics, Faculty of Mathematics and Natural Sciences, University of Jember, Jember, Indonesia

The International Journal of Biostatistics, 2024, vol. 20, issue 1, 245-278

Abstract: This paper considers a partially linear regression model relating a right-censored response variable to predictors and an extra covariate with measured error. The main problem here is that censorship and measurement error problems need to be solved to estimate the model correctly. In this sense, we propose three modified semiparametric estimators obtained from local polynomial regression, kernel smoothing, and B-spline smoothing methods based on kernel deconvolution approach and synthetic data transformation. Here, kernel deconvolution technique is used to solve the measurement error problem in the model and synthetic data transformation is considered to add the effect of censorship to the estimation procedure, which is a very common method in the literature. The performances of the introduced estimators are compared in the detailed Monte-Carlo simulation study. In addition, Carotid endarterectomy data is used as real-world data example and results are presented. According to the results, it is seen that the deconvoluted local polynomial method gives more qualified estimates than other two methods.

Keywords: B-spline; deconvolution; kernel smoothing; local polynomial; synthetic data (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/ijb-2022-0044 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:ijbist:v:20:y:2024:i:1:p:245-278:n:1006

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/ijb/html

DOI: 10.1515/ijb-2022-0044

Access Statistics for this article

The International Journal of Biostatistics is currently edited by Antoine Chambaz, Alan E. Hubbard and Mark J. van der Laan

More articles in The International Journal of Biostatistics from De Gruyter
Bibliographic data for series maintained by Peter Golla ().