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<p>This paper investigates the problem of variable selection in joint mean and variance models under high-dimensional settings within the transfer learning framework. The primary goal is to enhance parameter estimation and prediction accuracy for the target dataset by leveraging source datasets that share similarities with the target. First, a two-step transfer learning method based on the <i>Lasso</i> penalty is proposed for scenarios where the transferable source datasets are known. Second, for situations where transferable sources are unknown, an algorithm-independent, data-driven source selection method is introduced. This approach effectively distinguishes between transferable and nontransferable sources and improves prediction performance on the target dataset by utilizing the identified transferable sources. Extensive simulation studies demonstrate the superior performance of the proposed methods. Furthermore, experiments on real-world datasets highlight their practical applicability and effectiveness.</p>

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Transfer learning for joint mean and variance model

  • 期刊论文

  • 发表日期：

    2026年3月29日

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  • Yuxin Wang,
  • Yunquan Song,
  • Rui Yang

摘要

This paper investigates the problem of variable selection in joint mean and variance models under high-dimensional settings within the transfer learning framework. The primary goal is to enhance parameter estimation and prediction accuracy for the target dataset by leveraging source datasets that share similarities with the target. First, a two-step transfer learning method based on the Lasso penalty is proposed for scenarios where the transferable source datasets are known. Second, for situations where transferable sources are unknown, an algorithm-independent, data-driven source selection method is introduced. This approach effectively distinguishes between transferable and nontransferable sources and improves prediction performance on the target dataset by utilizing the identified transferable sources. Extensive simulation studies demonstrate the superior performance of the proposed methods. Furthermore, experiments on real-world datasets highlight their practical applicability and effectiveness.