IDEAS home Printed from https://ideas.repec.org/a/wly/apsmbi/v36y2020i2p237-250.html
   My bibliography  Save this article

Bayesian nonparametric estimation of first passage distributions in semi‐Markov processes

Author

Listed:
  • Richard L. Warr
  • Travis B. Woodfield

Abstract

Bayesian nonparametric (BNP) models provide a flexible tool in modeling many processes. One area that has not yet utilized BNP estimation is semi‐Markov processes (SMPs). SMPs require a significant amount of computation; this, coupled with the computation requirements for BNP models, has hampered any applications of SMPs using BNP estimation. This paper presents a modeling and computational approach for BNP estimation in semi‐Markov models, which includes a simulation study and an application of asthma patients' first passage from one state of control to another.

Suggested Citation

  • Richard L. Warr & Travis B. Woodfield, 2020. "Bayesian nonparametric estimation of first passage distributions in semi‐Markov processes," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 36(2), pages 237-250, March.
    • Handle: RePEc:wly:apsmbi:v:36:y:2020:i:2:p:237-250
    DOI: 10.1002/asmb.2486
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/asmb.2486
    Download Restriction: no
    File URL: https://libkey.io/10.1002/asmb.2486?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
    ---><---

    References listed on IDEAS

    as
    1. Paolo Bulla & Pietro Muliere, 2007. "Bayesian Nonparametric Estimation for Reinforced Markov Renewal Processes," Statistical Inference for Stochastic Processes, Springer, vol. 10(3), pages 283-303, October.
    2. Phelan, Michael J., 1990. "Estimating the transition probabilities from censored Markov renewal processes," Statistics & Probability Letters, Elsevier, vol. 10(1), pages 43-47, June.
    3. C. Yau & O. Papaspiliopoulos & G. O. Roberts & C. Holmes, 2011. "Bayesian non‐parametric hidden Markov models with applications in genomics," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(1), pages 37-57, January.
    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. Stefano Favaro & Antonio Lijoi & Igor Prünster, 2012. "On the stick–breaking representation of normalized inverse Gaussian priors," DEM Working Papers Series 008, University of Pavia, Department of Economics and Management.
    2. Laura Liu, 2017. "Density Forecasts in Panel Models: A semiparametric Bayesian Perspective," PIER Working Paper Archive 17-006, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 28 Apr 2017.
    3. Liverani, Silvia & Hastie, David I. & Azizi, Lamiae & Papathomas, Michail & Richardson, Sylvia, 2015. "PReMiuM: An R Package for Profile Regression Mixture Models Using Dirichlet Processes," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i07).
    4. Laura Liu, 2018. "Density Forecasts in Panel Data Models : A Semiparametric Bayesian Perspective," Finance and Economics Discussion Series 2018-036, Board of Governors of the Federal Reserve System (U.S.).
    5. Andrea Arfè & Stefano Peluso & Pietro Muliere, 2021. "The semi-Markov beta-Stacy process: a Bayesian non-parametric prior for semi-Markov processes," Statistical Inference for Stochastic Processes, Springer, vol. 24(1), pages 1-15, April.
    6. I. Votsi & G. Gayraud & V. S. Barbu & N. Limnios, 2021. "Hypotheses testing and posterior concentration rates for semi-Markov processes," Statistical Inference for Stochastic Processes, Springer, vol. 24(3), pages 707-732, October.
    7. F. Bartolucci & A. Farcomeni & F. Pennoni, 2014. "Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 433-465, September.
    8. Zheng, Jing & Yu, Dongjie & Zhu, Bin & Tong, Changqing, 2022. "Learning hidden Markov models with unknown number of states," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 594(C).
    9. Adam Persin & Ajay Jasr, 2016. "Twisting the Alive Particle Filter," Methodology and Computing in Applied Probability, Springer, vol. 18(2), pages 335-358, June.
    10. Ng, Jason & Forbes, Catherine S. & Martin, Gael M. & McCabe, Brendan P.M., 2013. "Non-parametric estimation of forecast distributions in non-Gaussian, non-linear state space models," International Journal of Forecasting, Elsevier, vol. 29(3), pages 411-430.
    11. repec:dau:papers:123456789/13438 is not listed on IDEAS
    12. Raffaele Argiento & Matteo Ruggiero, 2018. "Computational challenges and temporal dependence in Bayesian nonparametric models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(2), pages 231-238, June.
    13. Wang, Jiangzhou & Cui, Tingting & Zhu, Wensheng & Wang, Pengfei, 2023. "Covariate-modulated large-scale multiple testing under dependence," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).
    14. Xia, Ye-Mao & Tang, Nian-Sheng, 2019. "Bayesian analysis for mixture of latent variable hidden Markov models with multivariate longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 132(C), pages 190-211.
    15. Joseph Gardiner & Andrew Hogan & Margaret Holmes-Rovner & David Rovner & Lawrence Griffith & Joel Kupersmith, 1995. "Confidence Intervals for Cost - Effectiveness Ratios," Medical Decision Making, , vol. 15(3), pages 254-263, August.
    16. Boyuan Zhang, 2020. "Forecasting with Bayesian Grouped Random Effects in Panel Data," Papers 2007.02435, arXiv.org, revised Oct 2020.
    17. Chopin, Nicolas & Gadat, Sébastien & Guedj, Benjamin & Guyader, Arnaud & Vernet, Elodie, 2015. "On some recent advances in high dimensional Bayesian Statistics," TSE Working Papers 15-557, Toulouse School of Economics (TSE).

    More about this item

    Statistics

    Access and download statistics

    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:wly:apsmbi:v:36:y:2020:i:2:p:237-250. 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: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1002/(ISSN)1526-4025 .

    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.