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Semi-parametric proportional hazards models with crossed random effects for psychometric response times

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Abstract
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.
Keywords
Bayesian estimation, response time, semi-parametric proportional hazards model, frailty model, crossed random effects, FRAMEWORK, ACCURACY, HETEROGENEITY, DISTRIBUTIONS, TRUNCATION, REGRESSION, FRAILTY, SPEED

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MLA
Loeys, Tom, et al. “Semi-Parametric Proportional Hazards Models with Crossed Random Effects for Psychometric Response Times.” BRITISH JOURNAL OF MATHEMATICAL & STATISTICAL PSYCHOLOGY, vol. 67, no. 2, 2014, pp. 304–27, doi:10.1111/bmsp.12020.
APA
Loeys, T., Legrand, C., Schettino, A., & Pourtois, G. (2014). Semi-parametric proportional hazards models with crossed random effects for psychometric response times. BRITISH JOURNAL OF MATHEMATICAL & STATISTICAL PSYCHOLOGY, 67(2), 304–327. https://doi.org/10.1111/bmsp.12020
Chicago author-date
Loeys, Tom, Catherine Legrand, Antonio Schettino, and Gilles Pourtois. 2014. “Semi-Parametric Proportional Hazards Models with Crossed Random Effects for Psychometric Response Times.” BRITISH JOURNAL OF MATHEMATICAL & STATISTICAL PSYCHOLOGY 67 (2): 304–27. https://doi.org/10.1111/bmsp.12020.
Chicago author-date (all authors)
Loeys, Tom, Catherine Legrand, Antonio Schettino, and Gilles Pourtois. 2014. “Semi-Parametric Proportional Hazards Models with Crossed Random Effects for Psychometric Response Times.” BRITISH JOURNAL OF MATHEMATICAL & STATISTICAL PSYCHOLOGY 67 (2): 304–327. doi:10.1111/bmsp.12020.
Vancouver
1.
Loeys T, Legrand C, Schettino A, Pourtois G. Semi-parametric proportional hazards models with crossed random effects for psychometric response times. BRITISH JOURNAL OF MATHEMATICAL & STATISTICAL PSYCHOLOGY. 2014;67(2):304–27.
IEEE
[1]
T. Loeys, C. Legrand, A. Schettino, and G. Pourtois, “Semi-parametric proportional hazards models with crossed random effects for psychometric response times,” BRITISH JOURNAL OF MATHEMATICAL & STATISTICAL PSYCHOLOGY, vol. 67, no. 2, pp. 304–327, 2014.
@article{4429144,
  abstract     = {{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.}},
  author       = {{Loeys, Tom and Legrand, Catherine and Schettino, Antonio and Pourtois, Gilles}},
  issn         = {{0007-1102}},
  journal      = {{BRITISH JOURNAL OF MATHEMATICAL & STATISTICAL PSYCHOLOGY}},
  keywords     = {{Bayesian estimation,response time,semi-parametric proportional hazards model,frailty model,crossed random effects,FRAMEWORK,ACCURACY,HETEROGENEITY,DISTRIBUTIONS,TRUNCATION,REGRESSION,FRAILTY,SPEED}},
  language     = {{eng}},
  number       = {{2}},
  pages        = {{304--327}},
  title        = {{Semi-parametric proportional hazards models with crossed random effects for psychometric response times}},
  url          = {{http://dx.doi.org/10.1111/bmsp.12020}},
  volume       = {{67}},
  year         = {{2014}},
}

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