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Modified Local Linear Estimators in Partially Linear Additive Models with Right-Censored Data Based on Different
Ersin Yılmaz1, Dursun Aydın1, S Ejaz Ahmed2
1Department of Statistics, Mugla Sıtkı Kocman University, Mugla 48000, Turkey.
Entropy (Basel, Switzerland)
|September 28, 2023
Summary
This study presents a modified local linear estimator for right-censored partially linear additive models. The new method offers a non-iterative solution, performing well with Kaplan-Meier weights and kNN imputation.
Area of Science:
- Statistics
- Biostatistics
- Econometrics
Background:
- Partially linear additive models (PLAM) provide a flexible framework for statistical modeling.
- Right-censored data is common in survival analysis and requires specialized estimation techniques.
- Existing methods for censored PLAM can be complex or limited in scope.
Purpose of the Study:
- To introduce a modified local linear estimator (LLR) for partially linear additive models (PLAM) with right-censored response variables.
- To develop a non-iterative estimation procedure for censored PLAM.
- To compare the performance of different methods for handling censored data within PLAM.
Main Methods:
- Utilized a modified local linear regression (LLR) approach.
- Employed a modified backfitting algorithm for non-iterative estimation.
- Investigated three methods for handling right-censorship: synthetic data transformation (ST), Kaplan-Meier weights (KMW), and kNN imputation (kNNI).
Main Results:
- The modified LLR provides a non-iterative solution for right-censored PLAM.
- Asymptotic properties of the estimators using ST and KMW were derived.
- Simulation studies and a real data example demonstrated the effectiveness of LLR, particularly with KMW and kNNI.
Conclusions:
- The proposed modified local linear estimator is a viable and efficient method for analyzing right-censored partially linear additive models.
- Kaplan-Meier weights and kNN imputation are effective strategies for addressing censoring in this context.
- The LLR approach offers practical advantages for real-world data analysis where censoring is present.
Keywords:
kNN imputationlocal linear regressionpartially linear additive modelsright-censored datasynthetic dataMore Related Videos
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