IDEAS home Printed from https://ideas.repec.org/a/spr/metron/v79y2021i2d10.1007_s40300-021-00208-7.html
   My bibliography  Save this article

Robust estimation with variational Bayes in presence of competing risks

Author

Listed:
  • Himanshu Rai

    (Banaras Hindu University)

  • Sanjeev K. Tomer

    (Banaras Hindu University)

  • Anoop Chaturvedi

    (University of Allahabad)

Abstract

Variational Bayes, a method from machine learning, can provide a good approximation to the intractable posterior density function. It converges fast and works efficiently for large data sets. In this paper, we employ this method for robust Bayesian estimation of cause-specific quantities using competing risk data with missing causes. We consider the contamination class of prior distributions for the concerned parameter and discuss the implementation of ML-II procedure of Good (Good thinking: the foundations of probability and its applications, University of Minnesota Press, Minnesota , 1983) through variational Bayes approach in order to select a prior in a data-dependent fashion leading to a robust posterior. We perform sensitivity analysis to observe the influence of prior on some posterior quantities of interest. We analyze a real data set of computer hard-drives having three competing causes of failure and illustrate that the considered method provides robust Bayes estimates of concerned parameters, cause-specific hazard, and cumulative incidence function.

Suggested Citation

  • Himanshu Rai & Sanjeev K. Tomer & Anoop Chaturvedi, 2021. "Robust estimation with variational Bayes in presence of competing risks," METRON, Springer;Sapienza Università di Roma, vol. 79(2), pages 207-223, August.
    • Handle: RePEc:spr:metron:v:79:y:2021:i:2:d:10.1007_s40300-021-00208-7
    DOI: 10.1007/s40300-021-00208-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40300-021-00208-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s40300-021-00208-7?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. Sanjib Basu & Ananda Sen & Mousumi Banerjee, 2003. "Bayesian analysis of competing risks with partially masked cause of failure," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 52(1), pages 77-93, January.
    2. Zhang, Fode & Shi, Yimin, 2016. "Geometry of exponential family with competing risks and censored data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 446(C), pages 234-245.
    3. Berger, J. & Berliner, L.M., 1984. "Bayesian input in Stein estimation and a new minimax empirical Bayes estimator," Journal of Econometrics, Elsevier, vol. 25(1-2), pages 87-108.
    4. David M. Blei & Alp Kucukelbir & Jon D. McAuliffe, 2017. "Variational Inference: A Review for Statisticians," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 859-877, April.
    5. Kuo, Lynn & Yang, Tae Young, 2000. "Bayesian reliability modeling for masked system lifetime data," Statistics & Probability Letters, Elsevier, vol. 47(3), pages 229-241, April.
    Full references (including those not matched with items on IDEAS)

    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. Shen Liu & Hongyan Liu, 2021. "Tagging Items Automatically Based on Both Content Information and Browsing Behaviors," INFORMS Journal on Computing, INFORMS, vol. 33(3), pages 882-897, July.
    2. Loaiza-Maya, Rubén & Smith, Michael Stanley & Nott, David J. & Danaher, Peter J., 2022. "Fast and accurate variational inference for models with many latent variables," Journal of Econometrics, Elsevier, vol. 230(2), pages 339-362.
    3. Xing Qin & Shuangge Ma & Mengyun Wu, 2023. "Two‐level Bayesian interaction analysis for survival data incorporating pathway information," Biometrics, The International Biometric Society, vol. 79(3), pages 1761-1774, September.
    4. Youngseon Lee & Seongil Jo & Jaeyong Lee, 2022. "A variational inference for the Lévy adaptive regression with multiple kernels," Computational Statistics, Springer, vol. 37(5), pages 2493-2515, November.
    5. Nathaniel Tomasetti & Catherine Forbes & Anastasios Panagiotelis, 2019. "Updating Variational Bayes: Fast Sequential Posterior Inference," Monash Econometrics and Business Statistics Working Papers 13/19, Monash University, Department of Econometrics and Business Statistics.
    6. Gael M. Martin & David T. Frazier & Christian P. Robert, 2020. "Computing Bayes: Bayesian Computation from 1763 to the 21st Century," Monash Econometrics and Business Statistics Working Papers 14/20, Monash University, Department of Econometrics and Business Statistics.
    7. Djohan Bonnet & Tifenn Hirtzlin & Atreya Majumdar & Thomas Dalgaty & Eduardo Esmanhotto & Valentina Meli & Niccolo Castellani & Simon Martin & Jean-François Nodin & Guillaume Bourgeois & Jean-Michel P, 2023. "Bringing uncertainty quantification to the extreme-edge with memristor-based Bayesian neural networks," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    8. Ho, Paul, 2023. "Global robust Bayesian analysis in large models," Journal of Econometrics, Elsevier, vol. 235(2), pages 608-642.
    9. Liang, Xinbin & Liu, Zhuoxuan & Wang, Jie & Jin, Xinqiao & Du, Zhimin, 2023. "Uncertainty quantification-based robust deep learning for building energy systems considering distribution shift problem," Applied Energy, Elsevier, vol. 337(C).
    10. Daniel Nevo & Reiko Nishihara & Shuji Ogino & Molin Wang, 2018. "The competing risks Cox model with auxiliary case covariates under weaker missing-at-random cause of failure," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(3), pages 425-442, July.
    11. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.
    12. Seokhyun Chung & Raed Al Kontar & Zhenke Wu, 2022. "Weakly Supervised Multi-output Regression via Correlated Gaussian Processes," INFORMS Joural on Data Science, INFORMS, vol. 1(2), pages 115-137, October.
    13. Gary Koop & Dimitris Korobilis, 2023. "Bayesian Dynamic Variable Selection In High Dimensions," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(3), pages 1047-1074, August.
    14. Kozumi, Hideo, 2004. "Posterior analysis of latent competing risk models by parallel tempering," Computational Statistics & Data Analysis, Elsevier, vol. 46(3), pages 441-458, June.
    15. Ziqi Zhang & Xinye Zhao & Mehak Bindra & Peng Qiu & Xiuwei Zhang, 2024. "scDisInFact: disentangled learning for integration and prediction of multi-batch multi-condition single-cell RNA-sequencing data," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    16. Dimitris Korobilis & Davide Pettenuzzo, 2020. "Machine Learning Econometrics: Bayesian algorithms and methods," Working Papers 2020_09, Business School - Economics, University of Glasgow.
    17. Thayer Alshaabi & David R Dewhurst & James P Bagrow & Peter S Dodds & Christopher M Danforth, 2021. "The sociospatial factors of death: Analyzing effects of geospatially-distributed variables in a Bayesian mortality model for Hong Kong," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-20, March.
    18. Bansal, Prateek & Krueger, Rico & Graham, Daniel J., 2021. "Fast Bayesian estimation of spatial count data models," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    19. Rogelio A. Mancisidor & Kjersti Aas, 2022. "Multimodal Generative Models for Bankruptcy Prediction Using Textual Data," Papers 2211.08405, arXiv.org, revised Feb 2024.
    20. Shota Gugushvili & Frank van der Meulen & Moritz Schauer & Peter Spreij, 2018. "Nonparametric Bayesian volatility estimation," Papers 1801.09956, arXiv.org, revised Mar 2019.

    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:metron:v:79:y:2021:i:2:d:10.1007_s40300-021-00208-7. 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.