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- Title
Right-censored partially linear regression model with error in variables: application with carotid endarterectomy dataset.
- Authors
Aydın, Dursun; Yılmaz, Ersin; Chamidah, Nur; Lestari, Budi
- 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.
- Subjects
ERRORS-in-variables models; MEASUREMENT errors; CAROTID endarterectomy; DECONVOLUTION (Mathematics); REGRESSION analysis; MONTE Carlo method; INDEPENDENT variables; CENSORING (Statistics)
- Publication
International Journal of Biostatistics, 2024, Vol 20, Issue 1, p245
- ISSN
1557-4679
- Publication type
Article
- DOI
10.1515/ijb-2022-0044