Lasso回归：从解释到预测

• 作者： 张沥今 ¹ 魏夏琰 ² 陆嘉琦 ² 潘俊豪 ¹
• 作者单位：
  1.  中山大学心理学系

  2.  浙江大学心理与行为科学系
• 提交时间：2020-05-14

摘要: 传统的最小二乘回归法关注于对当前数据集的准确估计，容易导致模型的过拟合，影响模型结论的可重复性。随着方法学领域的发展，涌现出的新兴统计工具可以弥补传统方法的局限，从过度关注回归系数值的解释转向提升研究结果的预测能力也愈加成为心理学领域重要的发展趋势。Lasso方法通过在模型估计中引入惩罚项的方式，可以获得更高的预测准确度和模型概化能力，同时也可以有效地处理过拟合和多重共线性问题，有助于心理学理论的构建和完善。

Abstract: Psychological researches focus on describing, explaining and predicting behavior, and having a good understanding of the association between variables is an essential part of this process. Regression analysis, a method to evaluate the relationship between variables, is widely used in psychological studies. However, due to its highly focus on the interpretation of sample data, the traditional ordinary least squares regression has several drawbacks, such as over-fitting problem and limitation on dealing with multicollinearity, which may undermine the generalizability of the model. These drawbacks have an inevitable influence on the promotion and prediction of the model conclusion. With the rapid development of methodology, Least absolute shrinkage and selection operator (Lasso) regression has been emerged to better compensate for the limitations of traditional methods. By introducing a penalty term in the model and shrinking the regression coefficients to zero, Lasso regression can achieve a higher accuracy of model prediction and model generalizability with the cost of a certain estimation bias. Besides, Lasso regression can also effectively deal with the multicollinearity problem. Therefore, it has been widely used in medicine, economics, neuroscience and other fields. In psychology, due to the limitations of computer computing power, researchers used to mainly rely on hypothesis testing to understand the association among variables to verify theories. Now, with the rapid development of machine learning, a shift from focusing on interpretation of the regression coefficients to improving the prediction of the model has emerged and become more and more important. Therefore, based on fundamental theories and real data analysis, the aim of this paper is to introduce the principles, implementation steps and advantages of the Lasso regression. With the help of statistic science, it is promising that more and more applied researchers will be called upon to focus on the emerging statistical tools to promote the development of psychology.

• 来自： 张沥今
• 期刊： 心理科学进展
• 分类： 心理学 >> 心理统计
• 引用： ChinaXiv:202005.00044 (或此版本 ChinaXiv:202005.00044V1)
  doi:10.12074/202005.00044
  CSTR:32003.36.ChinaXiv.202005.00044.V1
• 推荐引用方式： 张沥今,魏夏琰,陆嘉琦,潘俊豪.(2020).Lasso回归：从解释到预测.心理科学进展.[ChinaXiv:202005.00044] (点此复制)