IDEAS home Printed from https://ideas.repec.org/a/spr/aistmt/v46y1994i2p203-220.html
   My bibliography  Save this article

Bayesian and likelihood inference from equally weighted mixtures

Author

Listed:
  • Tom Leonard
  • John Hsu
  • Kam-Wah Tsui
  • James Murray

Abstract

No abstract is available for this item.

Suggested Citation

  • Tom Leonard & John Hsu & Kam-Wah Tsui & James Murray, 1994. "Bayesian and likelihood inference from equally weighted mixtures," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 46(2), pages 203-220, June.
    • Handle: RePEc:spr:aistmt:v:46:y:1994:i:2:p:203-220
    DOI: 10.1007/BF01720581
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/BF01720581
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/BF01720581?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. John Hsu & Tom Leonard & Kam-Wah Tsui, 1991. "Statistical inference for multiple choice tests," Psychometrika, Springer;The Psychometric Society, vol. 56(2), pages 327-348, June.
    2. Geweke, John, 1989. "Exact predictive densities for linear models with arch disturbances," Journal of Econometrics, Elsevier, vol. 40(1), pages 63-86, January.
    3. Yosihiko Ogata, 1990. "A Monte Carlo method for an objective Bayesian procedure," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 42(3), pages 403-433, September.
    4. R. DerSimonian, 1986. "Maximum Likelihood Estimation of a Mixing Distribution," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 35(3), pages 302-309, November.
    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. Chih-Wen Hsu & Marick Sinay & John Hsu, 2012. "Bayesian estimation of a covariance matrix with flexible prior specification," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(2), pages 319-342, April.

    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. Ardia, David & Hoogerheide, Lennart F., 2010. "Efficient Bayesian estimation and combination of GARCH-type models," MPRA Paper 22919, University Library of Munich, Germany.
    2. Oscar Andrés Espinosa Acuna & Paola Andrea Vaca González, 2017. "Ajuste de modelos garch clásico y bayesiano con innovaciones t—student para el índice COLCAP," Revista de Economía del Caribe 17172, Universidad del Norte.
    3. Gael M. Martin & David T. Frazier & Christian P. Robert, 2020. "Computing Bayes: Bayesian Computation from 1763 to the 21st Century," Monash Econometrics and Business Statistics Working Papers 14/20, Monash University, Department of Econometrics and Business Statistics.
    4. Manabu Asai & Michael McAleer, 2022. "Bayesian Analysis of Realized Matrix-Exponential GARCH Models," Computational Economics, Springer;Society for Computational Economics, vol. 59(1), pages 103-123, January.
    5. Leandro Maciel, 2021. "Cryptocurrencies value‐at‐risk and expected shortfall: Do regime‐switching volatility models improve forecasting?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 4840-4855, July.
    6. Liu, Xiaochun & Luger, Richard, 2015. "Unfolded GARCH models," Journal of Economic Dynamics and Control, Elsevier, vol. 58(C), pages 186-217.
    7. Nobuhiko Terui, 1997. "Forecasting Dynamic Market Share Relationships," Tinbergen Institute Discussion Papers 97-042/4, Tinbergen Institute.
    8. Reeves, Jonathan J., 2005. "Bootstrap prediction intervals for ARCH models," International Journal of Forecasting, Elsevier, vol. 21(2), pages 237-248.
    9. Staudenmayer, John & Ruppert, David & Buonaccorsi, John P., 2008. "Density Estimation in the Presence of Heteroscedastic Measurement Error," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 726-736, June.
    10. Tim Bollerslev & Ray Y. Chou & Narayanan Jayaraman & Kenneth F. Kroner - L, 1991. "es modéles ARCH en finance : un point sur la théorie et les résultats empiriques," Annals of Economics and Statistics, GENES, issue 24, pages 1-59.
    11. Sik-Yum Lee & Xin-Yuan Song, 2003. "Model comparison of nonlinear structural equation models with fixed covariates," Psychometrika, Springer;The Psychometric Society, vol. 68(1), pages 27-47, March.
    12. Charles S. Bos & Ronald J. Mahieu & Herman K. Van Dijk, 2000. "Daily exchange rate behaviour and hedging of currency risk," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(6), pages 671-696.
    13. Kleibergen, F.R. & Paap, R., 1996. "Priors, Posterior Odds and Lagrange Multiplier Statistics in Bayesian Analyses of Cointegration," Econometric Institute Research Papers EI 9668-/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    14. Bos, C.S. & Mahieu, R.J. & van Dijk, H.K., 2000. "On the variation of hedging decisions in daily currency risk management," Econometric Institute Research Papers EI 2000-20/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    15. Lanne, Markku & Luoto, Jani, 2008. "Robustness of the risk-return relationship in the U.S. stock market," Finance Research Letters, Elsevier, vol. 5(2), pages 118-127, June.
    16. Kleibergen, F.R. & Hoek, H., 1995. "Bayesian analysis of ARMA models using noninformative priors," Other publications TiSEM 81684a10-935f-49c4-b5ab-0, Tilburg University, School of Economics and Management.
    17. Wago, Hajime, 2004. "Bayesian estimation of smooth transition GARCH model using Gibbs sampling," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 64(1), pages 63-78.
    18. Ben Naceur, Hassen, 2014. "Stock Market Indexes: A random walk test with ARCH (q) disturbances," MPRA Paper 78978, University Library of Munich, Germany.
    19. Ardia, David & Bluteau, Keven & Boudt, Kris & Catania, Leopoldo, 2018. "Forecasting risk with Markov-switching GARCH models:A large-scale performance study," International Journal of Forecasting, Elsevier, vol. 34(4), pages 733-747.
    20. John Geweke, 1999. "Using simulation methods for bayesian econometric models: inference, development,and communication," Econometric Reviews, Taylor & Francis Journals, vol. 18(1), pages 1-73.

    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:46:y:1994:i:2:p:203-220. 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.