Transfer Learning for High-Dimensional Beta Regression Models

In the high-dimensional modeling problem of proportional response variables, how to achieve effective prediction and stable estimation with a limited number of target samples has always been an important topic in statistical modeling and transfer learning research. Based on the Beta regression model framework, this paper presents a two-parameter transfer modeling method that integrates Lasso regularization and multi-source transfer learning ideas. In response to the problem that source-task heterogeneity may cause negative transfer, this paper designs a likelihood transferable source detection algorithm, evaluates the transfer effect through threefold cross-validation, selects the source tasks that are beneficial to the target task, and jointly constructs the transfer model. In the simulation experiment, this paper assesses the performance of the proposed method under different transfer intensities and compares it with multiple benchmark methods to verify its robustness and effectiveness. This paper conducts an empirical study based on OECD regional well-being data, focusing on verifying the effectiveness and superiority of the proposed transferable source detection mechanism in migration modeling. The results show that the method proposed in this paper has strong transfer ability under small sample conditions, can effectively avoid negative transfer risks, and maintain high interpretability and stability while improving model performance.