发表机构
Florida State University; University of Wisconsin-Madison（佛罗里达州立大学; 威斯康星大学麦迪逊分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出一种数据分裂框架，用于增长曲线模型中的事后选择推断，通过高斯噪声分离选择与推断信息，提供有效推断并提高效率，应用实例验证其有效性。
AI 中文摘要
增长曲线模型在心理学研究中被广泛使用，变量选择有助于识别与纵向异质性相关的基线特征。然而，在数据驱动的变量选择之后进行常规推断可能无效，因为相同的结果数据既用于选择又用于推断。我们开发了一种用于增长曲线模型中事后选择推断的数据分裂框架，该框架通过添加和减去高斯噪声来分离用于选择和推断的信息，同时保留所有参与者在两个阶段中。该框架适应灵活的变量选择程序，并针对所选工作模型中的协方差加权线性投影参数。当协方差结构已知时，它提供精确推断，并且在协方差结构被估计时，在适当的正则条件下我们建立了渐近有效性。模拟表明，所提出的方法提供了有效的推断，并且相对于受试者层面的数据分裂提高了效率，而朴素的事后选择推断可能有偏。对美国青年纵向研究的应用说明了所提出的框架。
英文摘要
Growth curve models are widely used in psychological research, and variable selection can help identify baseline characteristics associated with longitudinal heterogeneity. However, conventional inference after data-driven variable selection can be invalid because the same outcome data are used for both selection and inference. We develop a data-fission framework for post-selection inference in growth-curve models that separates the information used for selection and inference while retaining all participants in both stages through the addition and subtraction of Gaussian noise. The framework accommodates flexible variable-selection procedures and targets covariance-weighted linear projection parameters in the selected working model. It provides exact inference when the covariance structure is known, and we establish asymptotic validity under suitable regularity conditions when the covariance structure is estimated. Simulations show that the proposed method provides valid inference and improves efficiency over subject-level data splitting, whereas na\"ıve post-selection inference can be biased. An application to the Longitudinal Study of American Youth illustrates the proposed framework.
