IDEAS home Printed from https://ideas.repec.org/a/spr/stpapr/v53y2012i4p833-848.html
   My bibliography  Save this article

A general threshold stress hybrid hazard model for lifetime data

Author

Listed:
  • Cynthia Tojeiro
  • Francisco Louzada

Abstract

In this paper we propose a hybrid hazard regression model with threshold stress which includes the proportional hazards and the accelerated failure time models as particular cases. To express the behavior of lifetimes the generalized-gamma distribution is assumed and an inverse power law model with a threshold stress is considered. For parameter estimation we develop a sampling-based posterior inference procedure based on Markov Chain Monte Carlo techniques. We assume proper but vague priors for the parameters of interest. A simulation study investigates the frequentist properties of the proposed estimators obtained under the assumption of vague priors. Further, some discussions on model selection criteria are given. The methodology is illustrated on simulated and real lifetime data set. Copyright Springer-Verlag 2012

Suggested Citation

  • Cynthia Tojeiro & Francisco Louzada, 2012. "A general threshold stress hybrid hazard model for lifetime data," Statistical Papers, Springer, vol. 53(4), pages 833-848, November.
    • Handle: RePEc:spr:stpapr:v:53:y:2012:i:4:p:833-848
    DOI: 10.1007/s00362-011-0386-1
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s00362-011-0386-1
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s00362-011-0386-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Gomes, O. & Combes, C. & Dussauchoy, A., 2008. "Parameter estimation of the generalized gamma distribution," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(4), pages 955-963.
    2. W. R. Gilks & P. Wild, 1992. "Adaptive Rejection Sampling for Gibbs Sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(2), pages 337-348, June.
    3. Hirose, Hideo, 2000. "Maximum likelihood parameter estimation by model augmentation with applications to the extended four-parameter generalized gamma distribution," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 54(1), pages 81-97.
    4. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Raffaele Argiento & Alessandra Guglielmi & Antonio Pievatolo, 2014. "Estimation, prediction and interpretation of NGG random effects models: an application to Kevlar fibre failure times," Statistical Papers, Springer, vol. 55(3), pages 805-826, August.
    2. Adriano Suzuki & Vicente Cancho & Francisco Louzada, 2016. "The Poisson–Inverse-Gaussian regression model with cure rate: a Bayesian approach and its case influence diagnostics," Statistical Papers, Springer, vol. 57(1), pages 133-159, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Min-Je Choi & Do-Hoon Kim, 2020. "Assessment and Management of Small Yellow Croaker ( Larimichthys polyactis ) Stocks in South Korea," Sustainability, MDPI, vol. 12(19), pages 1-17, October.
    2. Chibuzor Christopher Nnanatu & Glory Atilola & Paul Komba & Lubanzadio Mavatikua & Zhuzhi Moore & Dennis Matanda & Otibho Obianwu & Ngianga-Bakwin Kandala, 2021. "Evaluating changes in the prevalence of female genital mutilation/cutting among 0-14 years old girls in Nigeria using data from multiple surveys: A novel Bayesian hierarchical spatio-temporal model," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-31, February.
    3. Kaan Kuzu & Refik Soyer, 2018. "Bayesian modeling of abandonments in ticket queues," Naval Research Logistics (NRL), John Wiley & Sons, vol. 65(6-7), pages 499-521, September.
    4. S. Upadhyay & M. Peshwani, 2008. "Posterior analysis of lognormal regression models using the Gibbs sampler," Statistical Papers, Springer, vol. 49(1), pages 59-85, March.
    5. Ngianga-Bakwin Kandala & Chibuzor Christopher Nnanatu & Glory Atilola & Paul Komba & Lubanzadio Mavatikua & Zhuzhi Moore & Gerry Mackie & Bettina Shell-Duncan, 2019. "A Spatial Analysis of the Prevalence of Female Genital Mutilation/Cutting among 0–14-Year-Old Girls in Kenya," IJERPH, MDPI, vol. 16(21), pages 1-28, October.
    6. Song, J.J. & Ghosh, M. & Miaou, S. & Mallick, B., 2006. "Bayesian multivariate spatial models for roadway traffic crash mapping," Journal of Multivariate Analysis, Elsevier, vol. 97(1), pages 246-273, January.
    7. Naranjo, L. & Martín, J. & Pérez, C.J., 2014. "Bayesian binary regression with exponential power link," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 464-476.
    8. Yilmaz, Hulya & Sazak, Hakan S., 2014. "Double-looped maximum likelihood estimation for the parameters of the generalized gamma distribution," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 98(C), pages 18-30.
    9. Andrade, A.R. & Teixeira, P.F., 2015. "Statistical modelling of railway track geometry degradation using Hierarchical Bayesian models," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 169-183.
    10. Brown, Sarah & Ghosh, Pulak & Su, Li & Taylor, Karl, 2015. "Modelling household finances: A Bayesian approach to a multivariate two-part model," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 190-207.
    11. Zhang, Yue & Zhang, Bin, 2018. "Semiparametric spatial model for interval-censored data with time-varying covariate effects," Computational Statistics & Data Analysis, Elsevier, vol. 123(C), pages 146-156.
    12. Roy, Vivekananda, 2014. "Efficient estimation of the link function parameter in a robust Bayesian binary regression model," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 87-102.
    13. Susanne Gschlößl & Claudia Czado, 2008. "Modelling count data with overdispersion and spatial effects," Statistical Papers, Springer, vol. 49(3), pages 531-552, July.
    14. Radu Tunaru, 2015. "Model Risk in Financial Markets:From Financial Engineering to Risk Management," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 9524, August.
    15. Refik Soyer & M. Murat Tarimcilar, 2008. "Modeling and Analysis of Call Center Arrival Data: A Bayesian Approach," Management Science, INFORMS, vol. 54(2), pages 266-278, February.
    16. Wenchen Liu & Yincai Tang & Ancha Xu, 2021. "Zero-and-one-inflated Poisson regression model," Statistical Papers, Springer, vol. 62(2), pages 915-934, April.
    17. Combes, Catherine & Ng, Hon Keung Tony, 2022. "On parameter estimation for Amoroso family of distributions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 191(C), pages 309-327.
    18. Franta, Michal, 2017. "Rare shocks vs. non-linearities: What drives extreme events in the economy? Some empirical evidence," Journal of Economic Dynamics and Control, Elsevier, vol. 75(C), pages 136-157.
    19. Xin Wang & Emily Berg & Zhengyuan Zhu & Dongchu Sun & Gabriel Demuth, 2018. "Small Area Estimation of Proportions with Constraint for National Resources Inventory Survey," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(4), pages 509-528, December.
    20. Simon Cheng & Yingmei Xi & Ming-Hui Chen, 2008. "A New Mixture Model for Misclassification With Applications for Survey Data," Sociological Methods & Research, , vol. 37(1), pages 75-104, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:stpapr:v:53:y:2012:i:4:p:833-848. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.