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2020 Model selection and model averaging for analysis of truncated and censored data with measurement error
Li-Pang Chen, Grace Y. Yi
Electron. J. Statist. 14(2): 4054-4109 (2020). DOI: 10.1214/20-EJS1762

Abstract

Model selection plays a critical role in statistical inference and a large literature has been devoted to this topic. Despite extensive research attention on model selection, research gaps still remain. An important but relatively unexplored problem concerns truncated and censored data with measurement error. Although analysis of left-truncated and right-censored (LTRC) data has received extensive interests in survival analysis, there has been no research on model selection for LTRC data with measurement error. In this paper, we take up this important problem and develop inferential procedures to handle model selection for LTRC data with measurement error in covariates. Our development employs the local model misspecification framework ([6]; [10]) and emphasizes the use of the focus information criterion (FIC). We develop valid estimators using the model averaging scheme and establish theoretical results to justify the validity of our methods. Numerical studies are conducted to assess the performance of the proposed methods.

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Li-Pang Chen. Grace Y. Yi. "Model selection and model averaging for analysis of truncated and censored data with measurement error." Electron. J. Statist. 14 (2) 4054 - 4109, 2020. https://doi.org/10.1214/20-EJS1762

Information

Received: 1 August 2019; Published: 2020
First available in Project Euclid: 4 November 2020

zbMATH: 07285580
MathSciNet: MR4170184
Digital Object Identifier: 10.1214/20-EJS1762

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Vol.14 • No. 2 • 2020
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