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Bayesian and profile likelihood change point methods for modeling cognitive function over time

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  • Hall, Charles B.
  • Ying, Jun
  • Kuo, Lynn
  • Lipton, Richard B.

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  • Hall, Charles B. & Ying, Jun & Kuo, Lynn & Lipton, Richard B., 2003. "Bayesian and profile likelihood change point methods for modeling cognitive function over time," Computational Statistics & Data Analysis, Elsevier, vol. 42(1-2), pages 91-109, February.
    • Handle: RePEc:eee:csdana:v:42:y:2003:i:1-2:p:91-109
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    References listed on IDEAS

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    1. Bradley P. Carlin & Alan E. Gelfand & Adrian F. M. Smith, 1992. "Hierarchical Bayesian Analysis of Changepoint Problems," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(2), pages 389-405, June.
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    Cited by:

    1. Perez, C.J. & Martin, J. & Rufo, M.J., 2006. "MCMC-based local parametric sensitivity estimations," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 823-835, November.
    2. Gebrenegus Ghilagaber & Parfait Munezero, 2020. "Bayesian change-point modelling of the effects of 3-points-for-a-win rule in football," Journal of Applied Statistics, Taylor & Francis Journals, vol. 47(2), pages 248-264, January.
    3. Pérez, C.J. & Martín, J. & Rufo, M.J., 2006. "Sensitivity estimations for Bayesian inference models solved by MCMC methods," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1310-1314.
    4. Zhao, L. & Banerjee, M., 2012. "Bayesian piecewise mixture model for racial disparity in prostate cancer progression," Computational Statistics & Data Analysis, Elsevier, vol. 56(2), pages 362-369.
    5. El-Bassiouni, M. Y. & Charif, H. A., 2004. "Testing a null variance ratio in mixed models with zero degrees of freedom for error," Computational Statistics & Data Analysis, Elsevier, vol. 46(4), pages 707-719, July.
    6. Hélène Jacqmin-Gadda & Daniel Commenges & Jean-François Dartigues, 2006. "Random Changepoint Model for Joint Modeling of Cognitive Decline and Dementia," Biometrics, The International Biometric Society, vol. 62(1), pages 254-260, March.
    7. Alexander C. McLain & Paul S. Albert, 2014. "Modeling longitudinal data with a random change point and no time-zero: Applications to inference and prediction of the labor curve," Biometrics, The International Biometric Society, vol. 70(4), pages 1052-1060, December.
    8. Getachew A. Dagne, 2021. "Bayesian Quantile Bent-Cable Growth Models for Longitudinal Data with Skewness and Detection Limit," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(1), pages 129-141, April.
    9. Chenxi Li & N. Maritza Dowling & Rick Chappell, 2015. "Quantile regression with a change‐point model for longitudinal data: An application to the study of cognitive changes in preclinical alzheimer's disease," Biometrics, The International Biometric Society, vol. 71(3), pages 625-635, September.
    10. G. Muniz Terrera & A. van den Hout & F. E. Matthews, 2011. "Random change point models: investigating cognitive decline in the presence of missing data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(4), pages 705-716, November.
    11. van den Hout, Ardo & Muniz-Terrera, Graciela & Matthews, Fiona E., 2013. "Change point models for cognitive tests using semi-parametric maximum likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 684-698.

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