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政大機構典藏 > 商學院 > 統計學系 > 期刊論文 > Item 140.119/142870

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/142870

Title:	Unbiased boosting estimation for censored survival data
Authors:	陳立榜 Chen, Li-Pang Yi, Grace
Contributors:	統計系
Keywords:	Adjusted loss functions;boosting;consistency;empirical processes;machine learning;right-censoring;survival data
Date:	2022-06
Issue Date:	2022-12-27 11:05:48 (UTC+8)
Abstract:	Boosting methods have been broadly discussed for various settings, and most methods handle data with complete observations. Although some methods are available for survival data with censored responses, they tend to assume a specific model for the survival process, and most provide numerical implementation procedures without rigorous theoretical justifications. In this paper, we develop an unbiased boosting estimation method for censored survival data, without assuming an explicit model, and explore three strategies for adjusting the loss functions, while accommodating censoring effects. We implement the proposed method using a functional gradient descent algorithm, and rigorously establish our theoretical results, including the consistency and optimization convergence. Our numerical studies show that the proposed method exhibits satisfactory performance in finite-sample settings.
Relation:	Statistica Sinica
Data Type:	article
DOI 連結:	https://doi.org/10.5705/ss.202021.0050
DOI:	10.5705/ss.202021.0050
Appears in Collections:	[統計學系] 期刊論文

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