Wu Jou-Chin, Chen Li-Pang
Department of Statistics, National Chengchi University, Taipei, Taiwan, ROC.
Stat Med. 2025 Jul;44(15-17):e70163. doi: 10.1002/sim.70163.
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.
泊松回归模型一直是刻画计数响应和协变量的常用方法。随着数据收集的快速发展，额外的源信息可以很容易地被记录下来。为了有效地利用源数据来改进原始数据下的估计，迁移学习方法被视为一种策略。然而，给定数据集中存在的挑战性问题包括测量误差和变量的高维性，这些在迁移学习的背景下尚未得到充分探索。在本文中，我们提出了一种新颖的策略来处理容易出错的计数响应，并通过使用源数据估计测量误差模型中的参数，然后采用迁移学习方法来推导校正估计量。此外，为了提高预测并避免模型不确定性，我们进一步建立了模型平均策略。模拟和乳腺癌数据研究验证了所提方法的令人满意的性能以及处理测量误差的有效性。
