AI 中文总结
提出SoCIFR框架，适配匹配病例对照设计，经模拟验证性能，应用于社交媒体轨迹预测用户受机器人交互影响的易感性。
AI 中文摘要
在本文中，我们提出了一种用于标量型删失信息设计函数回归（Scalar-on Censored Informative-design Functional Regression，简称SoCIFR）的新框架。该设置在现代纵向和数字数据应用中日益常见，但在函数数据文献中仍未得到充分发展。我们首先讨论SoCIFR中的估计与预测，并将该方法扩展以适配匹配的病例对照设计。该方法进一步推广至处理多个函数预测因子，可同时处理删失与未删失轨迹，这些轨迹在信息或非信息抽样设计下被观测。通过模拟研究，我们评估了所提方法在不同数据生成场景下的性能。我们将该方法应用于实际场景，基于固定时间窗口内观测到的社交媒体行为轨迹，预测用户在未来不同时间范围内受自动化（“机器人”）交互影响的易感性。
英文摘要
In this manuscript, we propose a novel framework for Scalar-on Censored Informative-design Functional Regression or SoCIFR. This setting is increasingly common in modern longitudinal and digital data applications but remains underdeveloped in functional data literature. We first discuss estimation and prediction in SoCIFR and extend the methodology to accommodate a matched case-control design. The proposed methodology is further generalized to handle multiple functional predictors, allowing for both censored and uncensored trajectories, observed under informative or non-informative sampling designs. Through simulation studies, we assess the performance of the proposed methods under various data-generating scenarios. We apply the methods to the motivating application for predicting user susceptibility to automated ("bot") interactions at increasing future time horizons based on social media behavioral trajectories observed over fixed time windows.
