Penalized partial likelihood inference of proportional hazards latent trait models
Penalized partial likelihood inference of proportional hazards latent trait models
复制标题
比例风险潜在特征模型的惩罚部分似然推理
DOI：
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发表时间：
2017
影响因子：
2.6
通讯作者：
Hyeon
中科院分区：
文献类型：
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作者：
Hyeon
具有潜在特征变量的 Cox 比例风险模型（Ranger & Ortner, 2012, Br. J. Math. Stat. Psychol., 65, 334）在解释同一受试者的反应时间依赖性方面表现出了良好的前景。该模型允许使用非参数基线危险率灵活地调整响应时间分布的形状，同时允许通过指数回归对潜在变量进行参数推断。然而，模型的灵活性是以模型估计的复杂性显着增加为代价的。本研究的目的是提出一种新的估计方法来克服模型估计中的这一困难。新过程基于惩罚偏似然估计器，其中在存在惩罚函数的情况下使偏似然最大化。一系列模拟研究证实了所提出方法的潜力，这些模拟研究将比例风险潜在特质模型与心理和教育测试数据进行拟合。还说明了估计方法在分层框架中的应用（van der Linden, 2007, Psychometrika, 72, 287），以联合分析响应时间和准确性分数。
The Cox proportional hazards model with a latent trait variable (Ranger & Ortner, 2012, Br. J. Math. Stat. Psychol., 65, 334) has shown promise in accounting for the dependency of response times from the same examinee. The model allows flexibility in shapes of response time distributions using the non-parametric baseline hazard rate while allowing parametric inference about the latent variable via exponential regression. The flexibility of the model, however, comes at the price of a significant increase in the complexity of estimating the model. The purpose of this study is to propose a new estimation approach to overcome this difficulty in model estimation. The new procedure is based on the penalized partial likelihood estimator in which the partial likelihood is maximized in the presence of a penalty function. The potential of the proposed method is corroborated by a series of simulation studies for fitting the proportional hazards latent trait model to psychological and educational testing data. The application of the estimation method to the hierarchical framework (van der Linden, 2007, Psychometrika, 72, 287) is also illustrated for jointly analysing response times and accuracy scores.