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A class of mixtures of dependent tail-free processes

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  • A. Jara
  • T. E. Hanson

Abstract

We propose a class of dependent processes in which density shape is regressed on one or more predictors through conditional tail-free probabilities by using transformed Gaussian processes. A particular linear version of the process is developed in detail. The resulting process is flexible and easy to fit using standard algorithms for generalized linear models. The method is applied to growth curve analysis, evolving univariate random effects distributions in generalized linear mixed models, and median survival modelling with censored data and covariate-dependent errors. Copyright 2011, Oxford University Press.

Suggested Citation

  • A. Jara & T. E. Hanson, 2011. "A class of mixtures of dependent tail-free processes," Biometrika, Biometrika Trust, vol. 98(3), pages 553-566.
    • Handle: RePEc:oup:biomet:v:98:y:2011:i:3:p:553-566
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    File URL: http://hdl.handle.net/10.1093/biomet/asq082
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    Cited by:

    1. Li, Li & Hanson, Timothy E., 2014. "A Bayesian semiparametric regression model for reliability data using effective age," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 177-188.
    2. Andrés F. Barrientos & Alejandro Jara & Fernando A. Quintana, 2017. "Fully Nonparametric Regression for Bounded Data Using Dependent Bernstein Polynomials," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 806-825, April.
    3. Angela Noufaily & M. C. Jones, 2013. "Parametric quantile regression based on the generalized gamma distribution," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(5), pages 723-740, November.
    4. Antonio Lijoi & Bernardo Nipoti, 2014. "A Class of Hazard Rate Mixtures for Combining Survival Data From Different Experiments," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(506), pages 802-814, June.
    5. Li Li & Ji-Hyun Lee, 2017. "A latent promotion time cure rate model using dependent tail-free mixtures," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(3), pages 891-905, June.
    6. Chen, Kunzhi & Shen, Weining & Zhu, Weixuan, 2023. "Covariate dependent Beta-GOS process," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).
    7. Antonio Lijoi & Bernardo Nipoti, 2013. "A class of hazard rate mixtures for combining survival data from different experiments," DEM Working Papers Series 059, University of Pavia, Department of Economics and Management.
    8. Haiming Zhou & Timothy Hanson & Jiajia Zhang, 2017. "Generalized accelerated failure time spatial frailty model for arbitrarily censored data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(3), pages 495-515, July.

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