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Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/158844

Title:	Transfer learning for error-contaminated Poisson regression models
Authors:	陳立榜;吳柔瑾 Chen, Li-Pang;Wu, Jou-Chin
Contributors:	統計系
Keywords:	error-prone count variables;model averaging;prediction;variable selection
Date:	2025-07
Issue Date:	2025-08-21 09:33:12 (UTC+8)
Abstract:	Poisson regression model has been a popular approach to characterize the count response and the covariates. With the rapid development of data collections, the additional source information can be easily recorded. To efficiently use the source data to improve the estimation under the original data, the transfer learning method is considered a strategy. However, challenging issues from the given datasets include measurement error and high-dimensionality in variables, which are not well explored in the context of transfer learning. In this paper, we propose a novel strategy to handle error-prone count responses and estimate the parameters in measurement error models by using the source data, and then employ the transfer learning method to derive the corrected estimator. Moreover, to improve the prediction and avoid the model uncertainty, we further establish the model averaging strategy. Simulation and breast cancer data studies verify the satisfactory performance of the proposed method and the validity of handling measurement error.
Relation:	Statistics in Medicine, Vol.44, No.15-17, e70163
Data Type:	article
DOI 連結:	https://doi.org/10.1002/sim.70163
DOI:	10.1002/sim.70163
Appears in Collections:	[統計學系] 期刊論文

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