用动态高斯过程建模随时间变化的潜变量，提升心理与社会调查分析精度。
A Dynamic, Ordinal Gaussian Process Item Response Theoretic Model
- 结合贝叶斯非参数IRT与高斯过程，灵活捕捉潜变量动态变化
- 在模拟和真实数据中均优于传统动态IRT模型，尤其在复杂轨迹下表现更佳
- 适合研究公众态度、意识形态等随时间演变的社会科学问题
社会科学家常需利用有序指标估计随时间变化的潜变量。通常采用项目反应理论（IRT）模型描述潜变量与观测指标的关系。本文结合贝叶斯非参数IRT的最新进展（对项目反应函数形状假设最少）与高斯过程时间序列方法，从纵向观测中捕捉潜变量的动态结构。提出广义动态高斯过程项目反应理论（GD-GPIRT）模型，并设计马尔可夫链蒙特卡洛采样算法以同时估计潜变量与反应函数。通过模拟研究评估其性能，对比了多种动态IRT基线模型；并应用于多个实质性研究，包括评估公众对经济与环境议题的态度，以及国会成员在堕胎争议中的意识形态演变。
原文摘要 · Abstract (English)
Social scientists are often interested in using ordinal indicators to estimate latent traits that change over time. Frequently, this is done with item response theoretic (IRT) models that describe the relationship between those latent traits and observed indicators. We combine recent advances in Bayesian nonparametric IRT, which makes minimal assumptions on shapes of item response functions, and Gaussian process time series methods to capture dynamic structures in latent traits from longitudinal observations. We propose a generalized dynamic Gaussian process item response theory (GD-GPIRT) as well as a Markov chain Monte Carlo sampling algorithm for estimation of both latent traits and response functions. We evaluate GD-GPIRT in simulation studies against baselines in dynamic IRT, and apply it to various substantive studies, including assessing public opinions on economy environment and congressional ideology related to abortion debate.
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