IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v161y2020ics0167715220300237.html
   My bibliography  Save this article

Variational inference for multiplicative intensity models

Author

Listed:
  • Lau, John W.
  • Cripps, Edward
  • Hui, Wendy

Abstract

We extend variational inference approximation of probability density functions to multiplicative intensity functions. For Bayesian nonparametrics, this provides a computationally efficient alternative to the blocked Gibbs sampler described in Ishwaran and James (2004). Simulation results are presented to demonstrate performance.

Suggested Citation

  • Lau, John W. & Cripps, Edward & Hui, Wendy, 2020. "Variational inference for multiplicative intensity models," Statistics & Probability Letters, Elsevier, vol. 161(C).
    • Handle: RePEc:eee:stapro:v:161:y:2020:i:c:s0167715220300237
    DOI: 10.1016/j.spl.2020.108720
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167715220300237
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.spl.2020.108720?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. Ishwaran, Hemant & James, Lancelot F., 2004. "Computational Methods for Multiplicative Intensity Models Using Weighted Gamma Processes: Proportional Hazards, Marked Point Processes, and Panel Count Data," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 175-190, January.
    2. Lau, John W., 2006. "Bayesian semi-parametric modeling for mixed proportional hazard models with right censoring," Statistics & Probability Letters, Elsevier, vol. 76(7), pages 719-728, April.
    3. Albert Lo & Chung-Sing Weng, 1989. "On a class of Bayesian nonparametric estimates: II. Hazard rate estimates," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 41(2), pages 227-245, June.
    4. Braun, Michael & McAuliffe, Jon, 2010. "Variational Inference for Large-Scale Models of Discrete Choice," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 324-335.
    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. Arbel, Julyan & Lijoi, Antonio & Nipoti, Bernardo, 2016. "Full Bayesian inference with hazard mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 359-372.
    2. Nieto-Barajas, Luis E., 2014. "Bayesian semiparametric analysis of short- and long-term hazard ratios with covariates," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 477-490.
    3. Lau, John W., 2006. "Bayesian semi-parametric modeling for mixed proportional hazard models with right censoring," Statistics & Probability Letters, Elsevier, vol. 76(7), pages 719-728, April.
    4. Hajargasht, Gholamreza & Rao, D.S. Prasada, 2019. "Multilateral index number systems for international price comparisons: Properties, existence and uniqueness," Journal of Mathematical Economics, Elsevier, vol. 83(C), pages 36-47.
    5. Wang, Xiaofei & Wang, Bing Xing & Hong, Yili & Jiang, Pei Hua, 2021. "Degradation data analysis based on gamma process with random effects," European Journal of Operational Research, Elsevier, vol. 292(3), pages 1200-1208.
    6. Krueger, Rico & Rashidi, Taha H. & Vij, Akshay, 2020. "A Dirichlet process mixture model of discrete choice: Comparisons and a case study on preferences for shared automated vehicles," Journal of choice modelling, Elsevier, vol. 36(C).
    7. 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.
    8. Antonio Lijoi & Igor Pruenster & Stephen G. Walker, 2008. "Posterior analysis for some classes of nonparametric models," ICER Working Papers - Applied Mathematics Series 05-2008, ICER - International Centre for Economic Research.
    9. Jansen, Laurens & Block, Ruud & Stepankin, Vladimir, 1995. "Analysis of superconductivity in Ba1−xKxBiO3 on the basis of indirect-exchange mediated pairing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 219(3), pages 327-350.
    10. Asim Ansari & Yang Li & Jonathan Z. Zhang, 2018. "Probabilistic Topic Model for Hybrid Recommender Systems: A Stochastic Variational Bayesian Approach," Marketing Science, INFORMS, vol. 37(6), pages 987-1008, November.
    11. 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.
    12. van Noortwijk, J.M., 2009. "A survey of the application of gamma processes in maintenance," Reliability Engineering and System Safety, Elsevier, vol. 94(1), pages 2-21.
    13. Gholamreza Hajargasht & William E. Griffiths, 2018. "Estimation and testing of stochastic frontier models using variational Bayes," Journal of Productivity Analysis, Springer, vol. 50(1), pages 1-24, October.
    14. Martin, Gael M. & Frazier, David T. & Maneesoonthorn, Worapree & Loaiza-Maya, Rubén & Huber, Florian & Koop, Gary & Maheu, John & Nibbering, Didier & Panagiotelis, Anastasios, 2024. "Bayesian forecasting in economics and finance: A modern review," International Journal of Forecasting, Elsevier, vol. 40(2), pages 811-839.
    15. 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.
    16. Luca La Rocca, 2008. "Bayesian Non‐Parametric Estimation of Smooth Hazard Rates for Seismic Hazard Assessment," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(3), pages 524-539, September.
    17. Fard, Farzad Alavi, 2015. "Analytical pricing of vulnerable options under a generalized jump–diffusion model," Insurance: Mathematics and Economics, Elsevier, vol. 60(C), pages 19-28.
    18. Fard, Farzad Alavi & Siu, Tak Kuen, 2013. "Pricing participating products with Markov-modulated jump–diffusion process: An efficient numerical PIDE approach," Insurance: Mathematics and Economics, Elsevier, vol. 53(3), pages 712-721.
    19. 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.
    20. 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.

    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:eee:stapro:v:161:y:2020:i:c:s0167715220300237. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    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.