标题： 精神科住院青少年自闭症攻击行为发作的分层时间点过程建模用于分支因子估计 显示英文标题

标题： Hierarchical Temporal Point Process Modeling of Aggressive Behavior Onset in Psychiatric Inpatient Youth with Autism for Branching Factor Estimation

摘要： 攻击性行为在自闭症住院青少年中经常以时间聚集的爆发形式出现，这使得区分外部诱因和内部升级变得复杂。 样本人群分支因子——由给定事件引发的新发作的预期数量——是行为动态中自我激发的关键总结。 之前的合并模型由于忽略了患者特异性变异而高估了这一数值。 我们通过使用带有指数核和边缘效应校正的分层霍克斯过程来解决这个问题，该过程允许在患者之间进行部分合并。 这种方法减少了高频个体的偏差，并稳定了数据稀疏个体的估计值。 贝叶斯推断使用了无U-turn采样器，模型评估通过收敛诊断、幂缩放敏感性分析和多个拟合优度（GOF）指标进行：PSIS-LOO、经过杜宾修改的刘易斯检验和基于随机时间变换定理（RTCT）的残差分析。 分层模型产生的分支因子估计均值（0.742 ± 0.026）显著低于合并模型（0.899 ± 0.015），并且比未合并模型（0.717 ± 0.139）的区间更窄。 这导致在分层模型下每次发作引发的事件级联减少了三倍。 敏感性分析证实了对先验和似然扰动的稳健性，而未合并模型对于数据稀疏的个体表现出不稳定性。 GOF指标始终支持或与分层模型相当。 带有边缘效应校正的分层霍克斯建模通过捕捉患者内和患者间的变异，提供了稳健的分支动态估计。 这使得内生事件与外生事件的分离更加清晰，支持与生理信号的关联，并增强了住院护理中的早期预警系统、个性化治疗和资源分配。 显示英文摘要

摘要： Aggressive behavior in autistic inpatient youth often arises in temporally clustered bursts complicating efforts to distinguish external triggers from internal escalation. The sample population branching factor-the expected number of new onsets triggered by a given event-is a key summary of self-excitation in behavior dynamics. Prior pooled models overestimate this quantity by ignoring patient-specific variability. We addressed this using a hierarchical Hawkes process with an exponential kernel and edge-effect correction allowing partial pooling across patients. This approach reduces bias from high-frequency individuals and stabilizes estimates for those with sparse data. Bayesian inference was performed using the No U-Turn Sampler with model evaluation via convergence diagnostics, power-scaling sensitivity analysis, and multiple Goodness-of-Fit (GOF) metrics: PSIS-LOO the Lewis test with Durbin's modification and residual analysis based on the Random Time Change Theorem (RTCT). The hierarchical model yielded a significantly lower and more precise branching factor estimate mean (0.742 +- 0.026) than the pooled model (0.899 +- 0.015) and narrower intervals than the unpooled model (0.717 +- 0.139). This led to a threefold smaller cascade of events per onset under the hierarchical model. Sensitivity analyses confirmed robustness to prior and likelihood perturbations while the unpooled model showed instability for sparse individuals. GOF measures consistently favored or on par to the hierarchical model. Hierarchical Hawkes modeling with edge-effect correction provides robust estimation of branching dynamics by capturing both within- and between-patient variability. This enables clearer separation of endogenous from exogenous events supports linkage to physiological signals and enhances early warning systems individualized treatment and resource allocation in inpatient care.