Monotone rank estimation of transformation models with length-biased and right-censored data

Chen, Xiaoping; Shi, Jianxin; Zhou, Yong

doi:10.1007/s11425-015-5035-z

Cited by 16 publications

(1 citation statement)

References 35 publications

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“…Several authors (Redelmeier and Singh, 2001;Han et al, 2011;Chen et al, 2015;Ma et al, 2016) are interested in finding out whether winning an Oscar Award causes the actor or actress' expected lifetime to increase.…”

Section: Real Datamentioning

confidence: 99%

High-Dimensional Variable Selection with Right Censored Length-biased Data

Zhou

Zou

2020

STAT SINICA

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Length-biased data are inevitably encountered in various fields ranging from epidemiological cohort studies to studies of labor economics, attracting much attention in the survival literature. A crucial goal of survival analysis is to identify a subset of risk factors and their risk contributions among massive clinical covariates. However, there has been no work on variable selection for length-biased data due to the complex nature of such data and the lack of a convenient loss function. In this article, we propose an estimation method based on the penalized estimating equations to obtain both sparse and consistent estimator for length-biased data under the accelerated failure time (AFT) model. The proposed estimator possesses selection and estimation consistency property. In particular, we implement the method with SCAD penalty and local linear approximation algorithm. We suggest selecting the tuning parameter by extended BIC criterion in high-dimensional setting. Furthermore, a novel multi-stage SCAD

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Section: Real Datamentioning

confidence: 99%

High-Dimensional Variable Selection with Right Censored Length-biased Data

Zhou

Zou

2020

STAT SINICA

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Revisiting the structural collapse of a 52.3 m composite wind turbine blade in a full‐scale bending test

Xiao

Zhao

2017

Wind Energy

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Full-scale structural tests enable an in-depth understanding of how composite blades respond to specific applied loads. Blade strength can be validated, and necessary modifications can be made to improve structural performance and/or reduce blade weight. This study revisits the structural collapse of a 52.3 m composite blade with new research content. Specifically, the present work examines the chain of events captured in the video record of the blade collapse and provides direct phenomenological evidence of how the blade collapsed in its ultimate limit state. In addition, three-dimensional strains are investigated by reconstructing the root transition region of the blade using solid brick elements in a finite element analysis. The strain components responsible for particular failure characteristics are identified. The structural response of the blade is investigated numerically. Interactive failure phenomena associated with strains, local buckling and material failure are examined in detail. The study shows that local buckling of the sandwich panels with unbalanced construction drives progressive failure of the composite materials and eventually leads to blade collapse owing to significant failure of the loadcarrying spar cap. Design modifications of the blade are proposed and validated with the test of a new blade. With respect to the latest DNV GL standard, this study notes a possible method to predict delamination and skin/core debonding failures. This study also recommends the use of three-dimensional solid elements in finite element analysis, especially when the strength and failure of large blades are of concern.

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The estimation for the general additive–multiplicative hazard model using the length-biased survival data

Zhou

2019

Stat Papers

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Monotone rank estimation of transformation models with length-biased and right-censored data

Cited by 16 publications

References 35 publications

High-Dimensional Variable Selection with Right Censored Length-biased Data

High-Dimensional Variable Selection with Right Censored Length-biased Data

Revisiting the structural collapse of a 52.3 m composite wind turbine blade in a full‐scale bending test

The estimation for the general additive–multiplicative hazard model using the length-biased survival data

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