Loeys Tom, Legrand Catherine, Schettino Antonio, Pourtois Gilles
Ghent University, Belgium.
Br J Math Stat Psychol. 2014 May;67(2):304-27. doi: 10.1111/bmsp.12020. Epub 2013 Aug 13.
The semi-parametric proportional hazards model with crossed random effects has two important characteristics: it avoids explicit specification of the response time distribution by using semi-parametric models, and it captures heterogeneity that is due to subjects and items. The proposed model has a proportionality parameter for the speed of each test taker, for the time intensity of each item, and for subject or item characteristics of interest. It is shown how all these parameters can be estimated by Markov chain Monte Carlo methods (Gibbs sampling). The performance of the estimation procedure is assessed with simulations and the model is further illustrated with the analysis of response times from a visual recognition task.
它通过使用半参数模型避免了对响应时间分布的明确指定，并且它捕捉了由于受试者和项目导致的异质性。所提出的模型为每个考生的速度、每个项目的时间强度以及感兴趣的受试者或项目特征都有一个比例参数。展示了如何通过马尔可夫链蒙特卡罗方法（吉布斯抽样）估计所有这些参数。通过模拟评估了估计程序的性能，并通过对视觉识别任务的响应时间分析进一步说明了该模型。
