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Modeling longitudinal data with a random change point and no time-zero: Applications to inference and prediction of the labor curve

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  • Alexander C. McLain
  • Paul S. Albert

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  • 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.
    • Handle: RePEc:bla:biomet:v:70:y:2014:i:4:p:1052-1060
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    File URL: http://hdl.handle.net/10.1111/biom.12218
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    References listed on IDEAS

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    1. 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.
    2. 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.
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    Cited by:

    1. Sungduk Kim & Olive D. Buhule & Paul S. Albert, 2019. "A Joint Model Approach for Longitudinal Data with no Time-Zero and Time-to-Event with Competing Risks," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(2), pages 449-464, July.

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