IDEAS home Printed from https://ideas.repec.org/a/spr/aistmt/v64y2012i1p107-133.html
   My bibliography  Save this article

Bayesian analysis of conditional autoregressive models

Author

Listed:
  • Victor Oliveira

Abstract

No abstract is available for this item.

Suggested Citation

  • Victor Oliveira, 2012. "Bayesian analysis of conditional autoregressive models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(1), pages 107-133, February.
    • Handle: RePEc:spr:aistmt:v:64:y:2012:i:1:p:107-133
    DOI: 10.1007/s10463-010-0298-1
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10463-010-0298-1
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10463-010-0298-1?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. Berger J.O. & De Oliveira V. & Sanso B., 2001. "Objective Bayesian Analysis of Spatially Correlated Data," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1361-1374, December.
    2. W. R. Gilks & N. G. Best & K. K. C. Tan, 1995. "Adaptive Rejection Metropolis Sampling Within Gibbs Sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 44(4), pages 455-472, December.
    3. A. F. Militino & M. D. Ugarte & L. García-Reinaldos, 2004. "Alternative Models for Describing Spatial Dependence among Dwelling Selling Prices," The Journal of Real Estate Finance and Economics, Springer, vol. 29(2), pages 193-209, September.
    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. Colette Mair & Sema Nickbakhsh & Richard Reeve & Jim McMenamin & Arlene Reynolds & Rory N Gunson & Pablo R Murcia & Louise Matthews, 2019. "Estimation of temporal covariances in pathogen dynamics using Bayesian multivariate autoregressive models," PLOS Computational Biology, Public Library of Science, vol. 15(12), pages 1-21, December.
    2. KWON, Heeeun & HWANG, Beom Seuk, 2023. "Do Spatial Characteristics Affect Housing Prices in Korea? : Evidence from Bayesian Spatial Models," Hitotsubashi Journal of Economics, Hitotsubashi University, vol. 64(2), pages 109-124, December.
    3. Cuirong Ren & Dongchu Sun, 2013. "Objective Bayesian analysis for CAR models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 65(3), pages 457-472, June.
    4. Ying C. MacNab, 2018. "Some recent work on multivariate Gaussian Markov random fields," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(3), pages 497-541, September.
    5. Getayeneh Antehunegn Tesema & Zemenu Tadesse Tessema & Stephane Heritier & Rob G. Stirling & Arul Earnest, 2023. "A Systematic Review of Joint Spatial and Spatiotemporal Models in Health Research," IJERPH, MDPI, vol. 20(7), pages 1-24, March.
    6. Haigang Liu & David B. Hitchcock & S. Zahra Samadi, 2020. "Spatio-temporal analysis of flood data from South Carolina," Journal of Statistical Distributions and Applications, Springer, vol. 7(1), pages 1-19, December.
    7. Hong-Ding Yang & Yun-Huan Lee & Che-Yang Lin, 2023. "On Study of the Occurrence of Earth-Size Planets in Kepler Mission Using Spatial Poisson Model," Mathematics, MDPI, vol. 11(11), pages 1-14, May.
    8. Ren, Cuirong & Sun, Dongchu, 2014. "Objective Bayesian analysis for autoregressive models with nugget effects," Journal of Multivariate Analysis, Elsevier, vol. 124(C), pages 260-280.

    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. Victor De Oliveira, 2009. "Bayesian Analysis Of Conditional Autoriegressive Models," Working Papers 0095, College of Business, University of Texas at San Antonio.
    2. Olivier Parent & James P. LeSage, 2008. "Using the variance structure of the conditional autoregressive spatial specification to model knowledge spillovers," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(2), pages 235-256.
    3. England, Peter, 2002. "Addendum to "Analytic and bootstrap estimates of prediction errors in claims reserving"," Insurance: Mathematics and Economics, Elsevier, vol. 31(3), pages 461-466, December.
    4. Juergen Deppner & Marcelo Cajias, 2024. "Accounting for Spatial Autocorrelation in Algorithm-Driven Hedonic Models: A Spatial Cross-Validation Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 68(2), pages 235-273, February.
    5. Acharki, Naoufal & Bertoncello, Antoine & Garnier, Josselin, 2023. "Robust prediction interval estimation for Gaussian processes by cross-validation method," Computational Statistics & Data Analysis, Elsevier, vol. 178(C).
    6. 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.
    7. Gabriele Fiorentini & Enrique Sentana & Neil Shephard, 2004. "Likelihood-Based Estimation of Latent Generalized ARCH Structures," Econometrica, Econometric Society, vol. 72(5), pages 1481-1517, September.
    8. Park, Eunchun & Brorsen, B. Wade & Harri, Ardian, 2016. "Using Bayesian Spatial Smoothing and Extreme Value Theory to Develop Area-Yield Crop Insurance Rating," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 235754, Agricultural and Applied Economics Association.
    9. Eric Yanchenko & Howard D. Bondell & Brian J. Reich, 2024. "Spatial regression modeling via the R2D2 framework," Environmetrics, John Wiley & Sons, Ltd., vol. 35(2), March.
    10. Zhang, Michael Yuanjie & Russell, Jeffrey R. & Tsay, Ruey S., 2008. "Determinants of bid and ask quotes and implications for the cost of trading," Journal of Empirical Finance, Elsevier, vol. 15(4), pages 656-678, September.
    11. Isabelle Grenier & Bruno Sansó & Jessica L. Matthews, 2024. "Multivariate nearest‐neighbors Gaussian processes with random covariance matrices," Environmetrics, John Wiley & Sons, Ltd., vol. 35(3), May.
    12. H. Abebe & F. Tan & G. Breukelen & M. Berger, 2014. "Robustness of Bayesian D-optimal design for the logistic mixed model against misspecification of autocorrelation," Computational Statistics, Springer, vol. 29(6), pages 1667-1690, December.
    13. Ren, Cuirong & Sun, Dongchu, 2014. "Objective Bayesian analysis for autoregressive models with nugget effects," Journal of Multivariate Analysis, Elsevier, vol. 124(C), pages 260-280.
    14. Manabu Asai & Michael McAleer & Jun Yu, 2006. "Multivariate Stochastic Volatility," Microeconomics Working Papers 22058, East Asian Bureau of Economic Research.
    15. Tu, Shiyi & Wang, Min & Sun, Xiaoqian, 2016. "Bayesian analysis of two-piece location–scale models under reference priors with partial information," Computational Statistics & Data Analysis, Elsevier, vol. 96(C), pages 133-144.
    16. Gunnar Taraldsen & Jarle Tufto & Bo H. Lindqvist, 2022. "Improper priors and improper posteriors," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(3), pages 969-991, September.
    17. Martellosio, Federico, 2008. "Testing for spatial autocorrelation: the regressors that make the power disappear," MPRA Paper 10542, University Library of Munich, Germany.
    18. Li, Kan & Luo, Sheng, 2019. "Bayesian functional joint models for multivariate longitudinal and time-to-event data," Computational Statistics & Data Analysis, Elsevier, vol. 129(C), pages 14-29.
    19. Ahmed Mustafa & Xiao Wei Zhang & Daniel G Aliaga & Martin Bruwier & Gen Nishida & Benjamin Dewals & Sébastian Erpicum & Pierre Archambeau & Michel Pirotton & Jacques Teller, 2020. "Procedural generation of flood-sensitive urban layouts," Environment and Planning B, , vol. 47(5), pages 889-911, June.
    20. Siem Jan Koopman & Neil Shephard & Jurgen A. Doornik, 1999. "Statistical algorithms for models in state space using SsfPack 2.2," Econometrics Journal, Royal Economic Society, vol. 2(1), pages 107-160.

    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:aistmt:v:64:y:2012:i:1:p:107-133. 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.