ORDINAL PROBIT FUNCTIONAL OUTCOME REGRESSION WITH APPLICATION TO COMPUTER-USE BEHAVIOR IN RHESUS MONKEYS.

IF 1.3 4区 数学 Q2 STATISTICS & PROBABILITY
Mark J Meyer, Jeffrey S Morris, Regina Paxton Gazes, Brent A Coull
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引用次数: 2

Abstract

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.

Abstract Image

序概率函数结果回归及其在恒河猴计算机使用行为中的应用。
泛函回归的研究在扩展到非高斯函数结果方面取得了很大进展,但对有序函数结果的探索仍然有限。基于对恒河猴计算机使用行为的研究,我们引入了有序概率函数结果回归模型(OPFOR)。OPFOR模型可以使用几种基函数中的一种进行拟合,包括惩罚b样条、小波和奥沙利文样条——最后一种通常表现最好。利用多种底层协方差模式进行的仿真表明,该模型在多个基函数下的估计效果相当好,联合可信区间的覆盖范围接近名义范围。最后,在应用中,我们使用贝叶斯模型选择标准来适应功能结果回归,以最好地表征一年中几个感兴趣的人口统计学因素与猴子计算机使用之间的关系。与标准的有序纵向分析相比,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和更细致的信息。
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来源期刊
Annals of Applied Statistics
Annals of Applied Statistics 社会科学-统计学与概率论
CiteScore
3.10
自引率
5.60%
发文量
131
审稿时长
6-12 weeks
期刊介绍: Statistical research spans an enormous range from direct subject-matter collaborations to pure mathematical theory. The Annals of Applied Statistics, the newest journal from the IMS, is aimed at papers in the applied half of this range. Published quarterly in both print and electronic form, our goal is to provide a timely and unified forum for all areas of applied statistics.
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