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Predicting times to event based on vine copula models

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  • Pan, Shenyi
  • Joe, Harry

Abstract

In statistics, time-to-event analysis methods traditionally focus on the estimation of hazards. In recent years, machine learning methods have been proposed to directly predict the event times. A method based on vine copula models is proposed to make point and interval predictions for a right-censored response variable given mixed discrete-continuous explanatory variables. Extensive experiments on simulated and real datasets show that the proposed vine copula approach provides a decent approximation to other time-to-event analysis models including proportional hazards and Weibull Accelerate Failure Time models. When the proportional hazards or Weibull Accelerate Failure Time assumptions do not hold, predictions based on vine copulas can significantly outperform other models, depending on the shape of the conditional quantile functions. This shows the flexibility of the proposed vine copula approach for general time-to-event datasets.

Suggested Citation

  • Pan, Shenyi & Joe, Harry, 2022. "Predicting times to event based on vine copula models," Computational Statistics & Data Analysis, Elsevier, vol. 175(C).
  • Handle: RePEc:eee:csdana:v:175:y:2022:i:c:s0167947322001268
    DOI: 10.1016/j.csda.2022.107546
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    References listed on IDEAS

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    1. Chang, Bo & Joe, Harry, 2019. "Prediction based on conditional distributions of vine copulas," Computational Statistics & Data Analysis, Elsevier, vol. 139(C), pages 45-63.
    2. Barthel, Nicole & Geerdens, Candida & Killiches, Matthias & Janssen, Paul & Czado, Claudia, 2018. "Vine copula based likelihood estimation of dependence patterns in multivariate event time data," Computational Statistics & Data Analysis, Elsevier, vol. 117(C), pages 109-127.
    3. Nicole Barthel & Candida Geerdens & Claudia Czado & Paul Janssen, 2019. "Dependence modeling for recurrent event times subject to right‐censoring with D‐vine copulas," Biometrics, The International Biometric Society, vol. 75(2), pages 439-451, June.
    4. Ulf Olsson & Fritz Drasgow & Neil Dorans, 1982. "The polyserial correlation coefficient," Psychometrika, Springer;The Psychometric Society, vol. 47(3), pages 337-347, September.
    5. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    6. Kraus, Daniel & Czado, Claudia, 2017. "D-vine copula based quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 110(C), pages 1-18.
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