Parametric methods, such as autoregressive models or latent growth modeling, are usually inflexible to model the dependence and nonlinear effects among the changes of latent traits whenever the time gap is irregular and the recorded time points are individually varying. Often in practice, the growth trend of latent traits is subject to certain monotone and smooth conditions. To incorporate such conditions and to alleviate the strong parametric assumption on regressing latent trajectories, a flexible nonparametric prior has been introduced to model the dynamic changes of latent traits for item response theory models over the study period. Suitable Bayesian computation schemes are developed for such analysis of the longitudinal and dichotomous item responses. Simulation studies and a real data example from educational testing have been used to illustrate our proposed methods.

中文翻译：

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项目反应理论模型中动态潜在特征的贝叶斯非参数单调回归

每当时间间隔不规则且记录的时间点分别变化时，参数方法（例如自回归模型或潜伏增长建模）通常就难以灵活地对潜伏性状变化之间的依赖性和非线性效应进行建模。在实践中，潜在性状的生长趋势经常受到某些单调和平稳条件的影响。为了纳入这样的条件并减轻对潜在轨迹回归的强参数假设，引入了一种灵活的非参数先验模型来为研究期内项目反应理论模型的潜在特征动态变化建模。针对纵向和二分项目响应的这种分析，开发了合适​​的贝叶斯计算方案。