IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v71y2015i2p376-385.html
   My bibliography  Save this article

An attraction–repulsion point process model for respiratory syncytial virus infections

Author

Listed:
  • Joshua Goldstein
  • Murali Haran
  • Ivan Simeonov
  • John Fricks
  • Francesca Chiaromonte

Abstract

No abstract is available for this item.

Suggested Citation

  • Joshua Goldstein & Murali Haran & Ivan Simeonov & John Fricks & Francesca Chiaromonte, 2015. "An attraction–repulsion point process model for respiratory syncytial virus infections," Biometrics, The International Biometric Society, vol. 71(2), pages 376-385, June.
    • Handle: RePEc:bla:biomet:v:71:y:2015:i:2:p:376-385
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/biom.12267
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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. Jones, Galin L. & Haran, Murali & Caffo, Brian S. & Neath, Ronald, 2006. "Fixed-Width Output Analysis for Markov Chain Monte Carlo," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1537-1547, December.
    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. James C. Russell & Ephraim M. Hanks & Murali Haran, 2016. "Dynamic Models of Animal Movement with Spatial Point Process Interactions," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(1), pages 22-40, March.
    2. Frédéric Lavancier & Jesper Møller, 2016. "Modelling Aggregation on the Large Scale and Regularity on the Small Scale in Spatial Point Pattern Datasets," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(2), pages 587-609, June.
    3. Ninna Vihrs & Jesper Møller & Alan E. Gelfand, 2022. "Approximate Bayesian inference for a spatial point process model exhibiting regularity and random aggregation," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(1), pages 185-210, 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. Jung, Yeun Ji & Hobert, James P., 2014. "Spectral properties of MCMC algorithms for Bayesian linear regression with generalized hyperbolic errors," Statistics & Probability Letters, Elsevier, vol. 95(C), pages 92-100.
    2. Zheng, Wei & Jin, Yong & Zhang, Guoyi, 2016. "Recursive estimation of time-average variance constants through prewhitening," Statistics & Probability Letters, Elsevier, vol. 114(C), pages 30-37.
    3. Peter L. Boveng & Jay M. Ver Hoef & David E. Withrow & Josh M. London, 2018. "A Bayesian Analysis of Abundance, Trend, and Population Viability for Harbor Seals in Iliamna Lake, Alaska," Risk Analysis, John Wiley & Sons, vol. 38(9), pages 1988-2009, September.
    4. Joshua Chan & Arnaud Doucet & Roberto León-González & Rodney W. Strachan, 2018. "Multivariate Stochastic Volatility with Co-Heteroscedasticity," Working Paper series 18-38, Rimini Centre for Economic Analysis.
    5. Johnson, Alicia A. & Jones, Galin L., 2015. "Geometric ergodicity of random scan Gibbs samplers for hierarchical one-way random effects models," Journal of Multivariate Analysis, Elsevier, vol. 140(C), pages 325-342.
    6. Li, Yong & Yu, Jun & Zeng, Tao, 2018. "Specification tests based on MCMC output," Journal of Econometrics, Elsevier, vol. 207(1), pages 237-260.
    7. Zhengyi Zhou & David S. Matteson & Dawn B. Woodard & Shane G. Henderson & Athanasios C. Micheas, 2015. "A Spatio-Temporal Point Process Model for Ambulance Demand," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 6-15, March.
    8. Vitoratou, Silia & Ntzoufras, Ioannis & Moustaki, Irini, 2016. "Explaining the behavior of joint and marginal Monte Carlo estimators in latent variable models with independence assumptions," LSE Research Online Documents on Economics 57685, London School of Economics and Political Science, LSE Library.
    9. Chen Ray-Bing & Chen Yi-Chi & Chu Chi-Hsiang & Lee Kuo-Jung, 2017. "On the determinants of the 2008 financial crisis: a Bayesian approach to the selection of groups and variables," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(5), pages 1-17, December.
    10. Sierra Pugh & Matthew J. Heaton & Jeff Svedin & Neil Hansen, 2019. "Spatiotemporal Lagged Models for Variable Rate Irrigation in Agriculture," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(4), pages 634-650, December.
    11. Roy, Vivekananda & Hobert, James P., 2010. "On Monte Carlo methods for Bayesian multivariate regression models with heavy-tailed errors," Journal of Multivariate Analysis, Elsevier, vol. 101(5), pages 1190-1202, May.
    12. Takaaki Koike & Mihoko Minami, 2017. "Estimation of Risk Contributions with MCMC," Papers 1702.03098, arXiv.org, revised Jan 2019.
    13. Takaaki Koike & Marius Hofert, 2020. "Markov Chain Monte Carlo Methods for Estimating Systemic Risk Allocations," Risks, MDPI, vol. 8(1), pages 1-33, January.
    14. James C. Russell & Ephraim M. Hanks & Murali Haran, 2016. "Dynamic Models of Animal Movement with Spatial Point Process Interactions," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(1), pages 22-40, March.
    15. Won Chang & Murali Haran & Patrick Applegate & David Pollard, 2016. "Calibrating an Ice Sheet Model Using High-Dimensional Binary Spatial Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(513), pages 57-72, March.
    16. Khare, Kshitij & Hobert, James P., 2012. "Geometric ergodicity of the Gibbs sampler for Bayesian quantile regression," Journal of Multivariate Analysis, Elsevier, vol. 112(C), pages 108-116.
    17. James C. Russell & Ephraim M. Hanks & Andreas P. Modlmeier & David P. Hughes, 2017. "Modeling Collective Animal Movement Through Interactions in Behavioral States," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(3), pages 313-334, September.
    18. Takaaki Koike & Marius Hofert, 2019. "Markov Chain Monte Carlo Methods for Estimating Systemic Risk Allocations," Papers 1909.11794, arXiv.org, revised May 2020.
    19. Masahiro Kuroda & Hiroki Hashiguchi & Shigekazu Nakagawa & Zhi Geng, 2013. "MCMC using Markov bases for computing $$p$$ -values in decomposable log-linear models," Computational Statistics, Springer, vol. 28(2), pages 831-850, April.
    20. Terrance Savitsky & Daniel McCaffrey, 2014. "Bayesian Hierarchical Multivariate Formulation with Factor Analysis for Nested Ordinal Data," Psychometrika, Springer;The Psychometric Society, vol. 79(2), pages 275-302, April.

    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:bla:biomet:v:71:y:2015:i:2:p:376-385. 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: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    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.