IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v65y2009i3p962-969.html
   My bibliography  Save this article

Comparison of Hierarchical Bayesian Models for Overdispersed Count Data using DIC and Bayes' Factors

Author

Listed:
  • Russell B. Millar

Abstract

No abstract is available for this item.

Suggested Citation

  • Russell B. Millar, 2009. "Comparison of Hierarchical Bayesian Models for Overdispersed Count Data using DIC and Bayes' Factors," Biometrics, The International Biometric Society, vol. 65(3), pages 962-969, September.
  • Handle: RePEc:bla:biomet:v:65:y:2009:i:3:p:962-969
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2008.01162.x
    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. Trevor C. Bailey & Paul J. Hewson, 2004. "Simultaneous modelling of multiple traffic safety performance indicators by using a multivariate generalized linear mixed model," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(3), pages 501-517, August.
    2. 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. Tenan, Simone & O’Hara, Robert B. & Hendriks, Iris & Tavecchia, Giacomo, 2014. "Bayesian model selection: The steepest mountain to climb," Ecological Modelling, Elsevier, vol. 283(C), pages 62-69.
    2. Chan, Joshua C.C. & Grant, Angelia L., 2016. "Fast computation of the deviance information criterion for latent variable models," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 847-859.
    3. Kurtis Shuler & Samuel Verbanic & Irene A. Chen & Juhee Lee, 2021. "A Bayesian nonparametric analysis for zero‐inflated multivariate count data with application to microbiome study," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(4), pages 961-979, August.
    4. R. B. Millar & S. McKechnie, 2014. "A one-step-ahead pseudo-DIC for comparison of Bayesian state-space models," Biometrics, The International Biometric Society, vol. 70(4), pages 972-980, December.
    5. Kathryn M. Irvine & T. J. Rodhouse & Ilai N. Keren, 2016. "Extending Ordinal Regression with a Latent Zero-Augmented Beta Distribution," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(4), pages 619-640, December.
    6. Brun, Mélanie & Abraham, Christophe & Jarry, Marc & Dumas, Jacques & Lange, Frédéric & Prévost, Etienne, 2011. "Estimating an homogeneous series of a population abundance indicator despite changes in data collection procedure: A hierarchical Bayesian modelling approach," Ecological Modelling, Elsevier, vol. 222(5), pages 1069-1079.
    7. Joshua C.C. Chan & Angelia L. Grant, 2014. "Issues in Comparing Stochastic Volatility Models Using the Deviance Information Criterion," CAMA Working Papers 2014-51, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    8. Joshua C. C. Chan & Eric Eisenstat, 2018. "Bayesian model comparison for time‐varying parameter VARs with stochastic volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(4), pages 509-532, June.
    9. Douglas Toledo & Cristiane Akemi Umetsu & Antonio Fernando Monteiro Camargo & Idemauro Antonio Rodrigues Lara, 2022. "Flexible models for non-equidispersed count data: comparative performance of parametric models to deal with underdispersion," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(3), pages 473-497, September.
    10. Ye Yang & Osman Doğan & Süleyman Taşpınar, 2023. "Observed-data DIC for spatial panel data models," Empirical Economics, Springer, vol. 64(3), pages 1281-1314, March.
    11. Kyu Ha Lee & Virginie Rondeau & Sebastien Haneuse, 2017. "Accelerated failure time models for semi‐competing risks data in the presence of complex censoring," Biometrics, The International Biometric Society, vol. 73(4), pages 1401-1412, December.
    12. Oludare Ariyo & Emmanuel Lesaffre & Geert Verbeke & Martijn Huisman & Martijn Heymans & Jos Twisk, 2022. "Bayesian model selection for multilevel mediation models," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 76(2), pages 219-235, May.
    13. Abadi, Fitsum & Barbraud, Christophe & Besson, Dominique & Bried, Joël & Crochet, Pierre-André & Delord, Karine & Forcada, Jaume & Grosbois, Vladimir & Phillips, Richard A. & Sagar, Paul & Thompson, P, 2014. "Importance of accounting for phylogenetic dependence in multi-species mark–recapture studies," Ecological Modelling, Elsevier, vol. 273(C), pages 236-241.
    14. Li, Yong & Yu, Jun & Zeng, Tao, 2020. "Deviance information criterion for latent variable models and misspecified models," Journal of Econometrics, Elsevier, vol. 216(2), pages 450-493.
    15. Abadi, Fitsum & Gimenez, Olivier & Jakober, Hans & Stauber, Wolfgang & Arlettaz, Raphaël & Schaub, Michael, 2012. "Estimating the strength of density dependence in the presence of observation errors using integrated population models," Ecological Modelling, Elsevier, vol. 242(C), pages 1-9.
    16. Fernanda B. Rizzato & Roseli A. Leandro & Clarice G.B. Demétrio & Geert Molenberghs, 2016. "A Bayesian approach to analyse overdispersed longitudinal count data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(11), pages 2085-2109, August.
    17. Li, Yong & Yu, Jun & Zeng, Tao, 2018. "Integrated Deviance Information Criterion for Latent Variable Models," Economics and Statistics Working Papers 6-2018, Singapore Management University, School of Economics.
    18. Iraj Kazemi & Fatemeh Hassanzadeh, 2021. "Marginalized random-effects models for clustered binomial data through innovative link functions," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(2), pages 197-228, June.
    19. Shonosuke Sugasawa & Kosuke Morikawa & Keisuke Takahata, 2022. "Bayesian semiparametric modeling of response mechanism for nonignorable missing data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(1), pages 101-117, March.
    20. Edgar C. Merkle & Daniel Furr & Sophia Rabe-Hesketh, 2019. "Bayesian Comparison of Latent Variable Models: Conditional Versus Marginal Likelihoods," Psychometrika, Springer;The Psychometric Society, vol. 84(3), pages 802-829, September.
    21. Oludare Ariyo & Emmanuel Lesaffre & Geert Verbeke & Adrian Quintero, 2022. "Bayesian Model Selection for Longitudinal Count Data," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(2), pages 516-547, November.
    22. Hui, Francis K.C., 2017. "Model-based simultaneous clustering and ordination of multivariate abundance data in ecology," Computational Statistics & Data Analysis, Elsevier, vol. 105(C), pages 1-10.

    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. Paul Hewson & Keming Yu, 2008. "Quantile regression for binary performance indicators," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 24(5), pages 401-418, September.
    2. Buddhavarapu, Prasad & Bansal, Prateek & Prozzi, Jorge A., 2021. "A new spatial count data model with time-varying parameters," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 566-586.
    3. Mumtaz, Haroon & Theodoridis, Konstantinos, 2017. "Common and country specific economic uncertainty," Journal of International Economics, Elsevier, vol. 105(C), pages 205-216.
    4. Jesse Elliott & Zemin Bai & Shu-Ching Hsieh & Shannon E Kelly & Li Chen & Becky Skidmore & Said Yousef & Carine Zheng & David J Stewart & George A Wells, 2020. "ALK inhibitors for non-small cell lung cancer: A systematic review and network meta-analysis," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-18, February.
    5. Christina Leuker & Thorsten Pachur & Ralph Hertwig & Timothy J. Pleskac, 2019. "Do people exploit risk–reward structures to simplify information processing in risky choice?," Journal of the Economic Science Association, Springer;Economic Science Association, vol. 5(1), pages 76-94, August.
    6. Francois Olivier & Laval Guillaume, 2011. "Deviance Information Criteria for Model Selection in Approximate Bayesian Computation," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-25, July.
    7. Raggi, Davide & Bordignon, Silvano, 2012. "Long memory and nonlinearities in realized volatility: A Markov switching approach," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3730-3742.
    8. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2023. "Large Time‐Varying Volatility Models for Hourly Electricity Prices," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 545-573, June.
    9. Rubio, F.J. & Steel, M.F.J., 2011. "Inference for grouped data with a truncated skew-Laplace distribution," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3218-3231, December.
    10. Alessandri, Piergiorgio & Mumtaz, Haroon, 2019. "Financial regimes and uncertainty shocks," Journal of Monetary Economics, Elsevier, vol. 101(C), pages 31-46.
    11. Padilla, Juan L. & Azevedo, Caio L.N. & Lachos, Victor H., 2018. "Multidimensional multiple group IRT models with skew normal latent trait distributions," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 250-268.
    12. Svetlana V. Tishkovskaya & Paul G. Blackwell, 2021. "Bayesian estimation of heterogeneous environments from animal movement data," Environmetrics, John Wiley & Sons, Ltd., vol. 32(6), September.
    13. David Macro & Jeroen Weesie, 2016. "Inequalities between Others Do Matter: Evidence from Multiplayer Dictator Games," Games, MDPI, vol. 7(2), pages 1-23, April.
    14. Tautenhahn, Susanne & Heilmeier, Hermann & Jung, Martin & Kahl, Anja & Kattge, Jens & Moffat, Antje & Wirth, Christian, 2012. "Beyond distance-invariant survival in inverse recruitment modeling: A case study in Siberian Pinus sylvestris forests," Ecological Modelling, Elsevier, vol. 233(C), pages 90-103.
    15. Julian P. T. Higgins & Simon G. Thompson & David J. Spiegelhalter, 2009. "A re‐evaluation of random‐effects meta‐analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 137-159, January.
    16. Simon Mak & Derek Bingham & Yi Lu, 2016. "A regional compound Poisson process for hurricane and tropical storm damage," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(5), pages 677-703, November.
    17. Xi, Yanhui & Peng, Hui & Qin, Yemei & Xie, Wenbiao & Chen, Xiaohong, 2015. "Bayesian analysis of heavy-tailed market microstructure model and its application in stock markets," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 117(C), pages 141-153.
    18. Huang, Zhaodong & Chien, Steven & Zhu, Wei & Zheng, Pengjun, 2022. "Scheduling wheel inspection for sustainable urban rail transit operation: A Bayesian approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    19. Jorge I. Figueroa-Zúñiga & Cristian L. Bayes & Víctor Leiva & Shuangzhe Liu, 2022. "Robust beta regression modeling with errors-in-variables: a Bayesian approach and numerical applications," Statistical Papers, Springer, vol. 63(3), pages 919-942, June.
    20. Leonardo Oliveira Martins & Hirohisa Kishino, 2010. "Distribution of distances between topologies and its effect on detection of phylogenetic recombination," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(1), pages 145-159, February.

    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:65:y:2009:i:3:p:962-969. 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.