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Abstract
The analysis of left truncated and right censored data is very common in survival and reliability analysis. In lifetime studies patients are often subject to left truncation in addition to right censoring. For example, in bone marrow transplant studies based on International Bone Marrow Transplant Registry (IBMTR), the patients who die while waiting for the transplants will not be reported to the IBMTR. In this paper, we develop novel U-statistics under left truncation and right censoring. We prove the √n-consistency of the proposed U-statistics. We derive the asymptotic distribution of the U-statistics using the counting process technique. As an application of the U-statistics, we develop a simple non-parametric test for testing the independence between time to failure and cause of failure in competing risks when the observations are subject to left truncation and right censoring. The finite sample performance of the proposed test is evaluated through a Monte Carlo simulation study. Finally, we illustrate our test procedure using the lifetime data of transformers. © 2023 Informa UK Limited, trading as Taylor & Francis Group.
Original language | English |
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Pages (from-to) | 900-917 |
Journal | Statistics |
Volume | 57 |
Issue number | 4 |
Online published | 24 May 2023 |
DOIs | |
Publication status | Published - 2023 |
Funding
"We thank the anonymous reviewers for their constructive comments on the earlier version of the manuscript which enabled us to improve substantially. This work was supported by Research Grant Council of Hong Kong (Grant number: 11200621) and Hong Kong Innovation and Technology Commission (InnoHK Project CIMDA). "
Research Keywords
- Competing risks
- left truncation
- right censoring
- U-statistics
Publisher's Copyright Statement
- COPYRIGHT TERMS OF DEPOSITED POSTPRINT FILE: This is an Accepted Manuscript of an article published by Taylor & Francis in Statistics on 24 May 2023, available online: http://www.tandfonline.com/10.1080/02331888.2023.2217314.
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GRF: New Approaches for Reliability Analysis of Industrial Systems Subject to Multivariate Degradation
XIE, M. (Principal Investigator / Project Coordinator) & Gaudoin, O. (Co-Investigator)
1/01/22 → …
Project: Research