Model averaging for right censored data with measurement error

This paper studies a novel model averaging estimation issue for linear regression models when the responses are right censored and the covariates are measured with error. A novel weighted Mallows-type criterion is proposed for the considered issue by introducing multiple candidate models. The weight...

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Bibliographic Details
Published in:Lifetime data analysis 2024-04, Vol.30 (2), p.501-527
Main Authors: Liang, Zhongqi, Zhang, Caiya, Xu, Linjun
Format: Article
Language:English
Subjects:
Aging
Asymptotic properties
Censored data (mathematics)
Computer Simulation
Criteria
Economics
Error analysis
Finance
Health Sciences
Humans
Insurance
Linear Models
Management
Mathematics and Statistics
Medicine
Methods
Operations Research/Decision Theory
Quality Control
Regression analysis
Regression models
Reliability
Safety and Risk
Simulation
Statistics
Statistics for Business
Statistics for Life Sciences
Survival analysis
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Description
Summary:This paper studies a novel model averaging estimation issue for linear regression models when the responses are right censored and the covariates are measured with error. A novel weighted Mallows-type criterion is proposed for the considered issue by introducing multiple candidate models. The weight vector for model averaging is selected by minimizing the proposed criterion. Under some regularity conditions, the asymptotic optimality of the selected weight vector is established in terms of its ability to achieve the lowest squared loss asymptotically. Simulation results show that the proposed method is superior to the other existing related methods. A real data example is provided to supplement the actual performance.
ISSN:1380-7870
1572-9249
1572-9249
DOI:10.1007/s10985-024-09620-3