序数Probit函数型结局回归及其在恒河猴计算机使用行为中的应用
Ordinal Probit Functional Outcome Regression with Application to Computer-Use Behavior in Rhesus Monkeys
AI总结：
针对序数函数型结局探索有限的问题，提出OPFOR模型，利用多种基函数进行拟合，并在模拟和恒河猴计算机使用行为应用中表现出优于传统模型的性能，提供更细致的信息。
摘要：
函数型回归研究在向非高斯函数型结局扩展方面取得了巨大进展，但对序数函数型结局的探索仍然有限。受恒河猴（Macaca mulatta）计算机使用行为研究的启发，我们引入了序数Probit函数型结局回归模型（OPFOR）。OPFOR模型可以使用包括惩罚B样条、小波和O'Sullivan样条在内的几种基函数之一进行拟合——其中后者通常表现最佳。使用多种潜在协方差模式进行的模拟表明，该模型在多种基函数下的估计表现相当好，联合可信区间的覆盖率接近名义水平。最后，在应用中，我们使用适应于函数型结局回归的贝叶斯模型选择准则，以最好地刻画几个感兴趣的人口统计因素与猴子一年中计算机使用之间的关系。与标准的序数纵向分析相比，OPFOR在模拟中优于累积链接混合效应模型，并提供了关于猴子计算机使用行为本质的额外且更细致的信息。
英文摘要：
Research in functional regression has made great strides in expanding to non-Gaussian functional outcomes, but exploration of ordinal functional outcomes remains limited. Motivated by a study of computer-use behavior in rhesus macaques (Macaca mulatta), we introduce the Ordinal Probit Functional Outcome Regression model (OPFOR). OPFOR models can be fit using one of several basis functions including penalized B-splines, wavelets, and O'Sullivan splines -- the last of which typically performs best. Simulation using a variety of underlying covariance patterns shows that the model performs reasonably well in estimation under multiple basis functions with near nominal coverage for joint credible intervals. Finally, in application, we use Bayesian model selection criteria adapted to functional outcome regression to best characterize the relation between several demographic factors of interest and the monkeys' computer use over the course of a year. In comparison with a standard ordinal longitudinal analysis, OPFOR outperforms a cumulative-link mixed-effects model in simulation and provides additional and more nuanced information on the nature of the monkeys' computer-use behavior.
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