IDEAS home Printed from https://ideas.repec.org/a/spr/stpapr/v49y2008i1p59-85.html
   My bibliography  Save this article

Posterior analysis of lognormal regression models using the Gibbs sampler

Author

Listed:
  • S. Upadhyay
  • M. Peshwani

Abstract

No abstract is available for this item.

Suggested Citation

  • S. Upadhyay & M. Peshwani, 2008. "Posterior analysis of lognormal regression models using the Gibbs sampler," Statistical Papers, Springer, vol. 49(1), pages 59-85, March.
    • Handle: RePEc:spr:stpapr:v:49:y:2008:i:1:p:59-85
    DOI: 10.1007/s00362-006-0372-1
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s00362-006-0372-1
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s00362-006-0372-1?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. Sylvia. Richardson & Peter J. Green, 1997. "On Bayesian Analysis of Mixtures with an Unknown Number of Components (with discussion)," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(4), pages 731-792.
    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.
    3. W. R. Gilks & P. Wild, 1992. "Adaptive Rejection Sampling for Gibbs Sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(2), pages 337-348, June.
    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. Enrico Fabrizi & Carlo Trivisano, 2016. "Bayesian Conditional Mean Estimation in Log-Normal Linear Regression Models with Finite Quadratic Expected Loss," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(4), pages 1064-1077, December.
    2. Rijji Sen & Rakesh Ranjan & S. K. Upadhyay, 2017. "A simple Bayes analysis of Weibull Based Accelerated Test model," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(1), pages 505-511, January.
    3. Christopher Withers & Saralees Nadarajah, 2012. "Unbiased estimates for a lognormal regression problem and a nonparametric alternative," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 75(2), pages 207-227, February.

    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. Cai, Jing-Heng & Song, Xin-Yuan & Lam, Kwok-Hap & Ip, Edward Hak-Sing, 2011. "A mixture of generalized latent variable models for mixed mode and heterogeneous data," Computational Statistics & Data Analysis, Elsevier, vol. 55(11), pages 2889-2907, November.
    2. Park, Byung-Jung & Zhang, Yunlong & Lord, Dominique, 2010. "Bayesian mixture modeling approach to account for heterogeneity in speed data," Transportation Research Part B: Methodological, Elsevier, vol. 44(5), pages 662-673, June.
    3. Kozumi, Hideo, 2004. "Posterior analysis of latent competing risk models by parallel tempering," Computational Statistics & Data Analysis, Elsevier, vol. 46(3), pages 441-458, June.
    4. Min-Je Choi & Do-Hoon Kim, 2020. "Assessment and Management of Small Yellow Croaker ( Larimichthys polyactis ) Stocks in South Korea," Sustainability, MDPI, vol. 12(19), pages 1-17, October.
    5. Yuan Fang & Dimitris Karlis & Sanjeena Subedi, 2022. "Infinite Mixtures of Multivariate Normal-Inverse Gaussian Distributions for Clustering of Skewed Data," Journal of Classification, Springer;The Classification Society, vol. 39(3), pages 510-552, November.
    6. Kazuhiko Kakamu, 2022. "Bayesian analysis of mixtures of lognormal distribution with an unknown number of components from grouped data," Papers 2210.05115, arXiv.org, revised Sep 2023.
    7. Chibuzor Christopher Nnanatu & Glory Atilola & Paul Komba & Lubanzadio Mavatikua & Zhuzhi Moore & Dennis Matanda & Otibho Obianwu & Ngianga-Bakwin Kandala, 2021. "Evaluating changes in the prevalence of female genital mutilation/cutting among 0-14 years old girls in Nigeria using data from multiple surveys: A novel Bayesian hierarchical spatio-temporal model," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-31, February.
    8. Carnicero, José Antonio, 2008. "A semi-parametric model for circular data based on mixtures of beta distributions," DES - Working Papers. Statistics and Econometrics. WS ws081305, Universidad Carlos III de Madrid. Departamento de Estadística.
    9. Kaan Kuzu & Refik Soyer, 2018. "Bayesian modeling of abandonments in ticket queues," Naval Research Logistics (NRL), John Wiley & Sons, vol. 65(6-7), pages 499-521, September.
    10. Ngianga-Bakwin Kandala & Chibuzor Christopher Nnanatu & Glory Atilola & Paul Komba & Lubanzadio Mavatikua & Zhuzhi Moore & Gerry Mackie & Bettina Shell-Duncan, 2019. "A Spatial Analysis of the Prevalence of Female Genital Mutilation/Cutting among 0–14-Year-Old Girls in Kenya," IJERPH, MDPI, vol. 16(21), pages 1-28, October.
    11. Assaf, A. George & Tsionas, Mike & Oh, Haemoon, 2018. "The time has come: Toward Bayesian SEM estimation in tourism research," Tourism Management, Elsevier, vol. 64(C), pages 98-109.
    12. Song, J.J. & Ghosh, M. & Miaou, S. & Mallick, B., 2006. "Bayesian multivariate spatial models for roadway traffic crash mapping," Journal of Multivariate Analysis, Elsevier, vol. 97(1), pages 246-273, January.
    13. Buddhavarapu, Prasad & Scott, James G. & Prozzi, Jorge A., 2016. "Modeling unobserved heterogeneity using finite mixture random parameters for spatially correlated discrete count data," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 492-510.
    14. Naranjo, L. & Martín, J. & Pérez, C.J., 2014. "Bayesian binary regression with exponential power link," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 464-476.
    15. Andrade, A.R. & Teixeira, P.F., 2015. "Statistical modelling of railway track geometry degradation using Hierarchical Bayesian models," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 169-183.
    16. Brown, Sarah & Ghosh, Pulak & Su, Li & Taylor, Karl, 2015. "Modelling household finances: A Bayesian approach to a multivariate two-part model," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 190-207.
    17. Zhang, Yue & Zhang, Bin, 2018. "Semiparametric spatial model for interval-censored data with time-varying covariate effects," Computational Statistics & Data Analysis, Elsevier, vol. 123(C), pages 146-156.
    18. Dingjing Shi & Xin Tong, 2017. "The Impact of Prior Information on Bayesian Latent Basis Growth Model Estimation," SAGE Open, , vol. 7(3), pages 21582440177, August.
    19. Roy, Vivekananda, 2014. "Efficient estimation of the link function parameter in a robust Bayesian binary regression model," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 87-102.
    20. Susanne Gschlößl & Claudia Czado, 2008. "Modelling count data with overdispersion and spatial effects," Statistical Papers, Springer, vol. 49(3), pages 531-552, July.

    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:stpapr:v:49:y:2008:i:1:p:59-85. 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.