IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v54y2010i3p724-731.html
   My bibliography  Save this article

Implementing Bayesian predictive procedures: The K-prime and K-square distributions

Author

Listed:
  • Poitevineau, Jacques
  • Lecoutre, Bruno

Abstract

The implementation of Bayesian predictive procedures under standard normal models is considered. Two distributions are of particular interest, the K-prime and K-square distributions. They also give exact inferences for simple and multiple correlation coefficients. Their cumulative distribution functions can be expressed in terms of infinite series of multiples of incomplete beta function ratios, thus adequate for recursive calculations. Efficient algorithms are provided. To deal with special cases where possible underflows may prevent a recurrence to work properly, a simple solution is proposed which results in a procedure which is intermediate between two classes of algorithm. Some examples of applications are given.

Suggested Citation

  • Poitevineau, Jacques & Lecoutre, Bruno, 2010. "Implementing Bayesian predictive procedures: The K-prime and K-square distributions," Computational Statistics & Data Analysis, Elsevier, vol. 54(3), pages 724-731, March.
  • Handle: RePEc:eee:csdana:v:54:y:2010:i:3:p:724-731
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(08)00541-0
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    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. Benton, Denise & Krishnamoorthy, K., 2003. "Computing discrete mixtures of continuous distributions: noncentral chisquare, noncentral t and the distribution of the square of the sample multiple correlation coefficient," Computational Statistics & Data Analysis, Elsevier, vol. 43(2), pages 249-267, June.
    2. Inoue, Lurdes Y.T. & Berry, Donald A. & Parmigiani, Giovanni, 2005. "Relationship Between Bayesian and Frequentist Sample Size Determination," The American Statistician, American Statistical Association, vol. 59, pages 79-87, February.
    3. Bruno Lecoutre & Jean‐Luc Guigues & Jacques Poitevineau, 1992. "Distribution of Quadratic Forms of Multivariate Generalized Student Variables," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(3), pages 617-627, November.
    4. H. Frick, 1990. "A Remark on Algorithm as 226: Computing Non‐Central Beta Probabilities," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 39(2), pages 311-312, June.
    5. David J. Spiegelhalter & Laurence S. Freedman & Mahesh K. B. Parmar, 1994. "Bayesian Approaches to Randomized Trials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 157(3), pages 357-387, May.
    6. Lecoutre, Marie-Paule & Rouanet, Henry, 1993. "Predictive Judgments in Situations of Statistical Analysis," Organizational Behavior and Human Decision Processes, Elsevier, vol. 54(1), pages 45-56, February.
    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. Steven E. Pav, 2015. "Inference on the Sharpe ratio via the upsilon distribution," Papers 1505.00829, arXiv.org, revised Aug 2021.

    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. Ali Baharev & Hermann Schichl & Endre Rév, 2017. "Computing the noncentral-F distribution and the power of the F-test with guaranteed accuracy," Computational Statistics, Springer, vol. 32(2), pages 763-779, June.
    2. Francesco De Pretis & Barbara Osimani, 2019. "New Insights in Computational Methods for Pharmacovigilance: E-Synthesis , a Bayesian Framework for Causal Assessment," IJERPH, MDPI, vol. 16(12), pages 1-19, June.
    3. Charles F. Manski, 2018. "Reasonable patient care under uncertainty," Health Economics, John Wiley & Sons, Ltd., vol. 27(10), pages 1397-1421, October.
    4. Jia‐Hau Guo & Lung‐Fu Chang, 2020. "Repeated Richardson extrapolation and static hedging of barrier options under the CEV model," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(6), pages 974-988, June.
    5. Yang, Nian & Chen, Nan & Liu, Yanchu & Wan, Xiangwei, 2017. "Approximate arbitrage-free option pricing under the SABR model," Journal of Economic Dynamics and Control, Elsevier, vol. 83(C), pages 198-214.
    6. Jos� Carlos Dias & João Pedro Vidal Nunes & João Pedro Ruas, 2015. "Pricing and static hedging of European-style double barrier options under the jump to default extended CEV model," Quantitative Finance, Taylor & Francis Journals, vol. 15(12), pages 1995-2010, December.
    7. Paul Gustafson & Nhu D. Le & Refik Saskin, 2001. "Case–Control Analysis with Partial Knowledge of Exposure Misclassification Probabilities," Biometrics, The International Biometric Society, vol. 57(2), pages 598-609, June.
    8. Charles F. Manski, 2021. "Econometrics for Decision Making: Building Foundations Sketched by Haavelmo and Wald," Econometrica, Econometric Society, vol. 89(6), pages 2827-2853, November.
    9. Izabela Oliveira & Daniel Ferreira, 2013. "Computing the noncentral gamma distribution, its inverse and the noncentrality parameter," Computational Statistics, Springer, vol. 28(4), pages 1663-1680, August.
    10. Charles F. Manski & Aleksey Tetenov, 2015. "Clinical trial design enabling ε-optimal treatment rules," CeMMAP working papers 60/15, Institute for Fiscal Studies.
    11. Peter Carr & Vadim Linetsky, 2006. "A jump to default extended CEV model: an application of Bessel processes," Finance and Stochastics, Springer, vol. 10(3), pages 303-330, September.
    12. Isakov, Leah & Lo, Andrew W. & Montazerhodjat, Vahid, 2019. "Is the FDA too conservative or too aggressive?: A Bayesian decision analysis of clinical trial design," Journal of Econometrics, Elsevier, vol. 211(1), pages 117-136.
    13. Jingjing Ye & Gregory Reaman, 2022. "Improving Early Futility Determination by Learning from External Data in Pediatric Cancer Clinical Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(2), pages 337-351, July.
    14. Dias, José Carlos & Vidal Nunes, João Pedro, 2018. "Universal recurrence algorithm for computing Nuttall, generalized Marcum and incomplete Toronto functions and moments of a noncentral χ2 random variable," European Journal of Operational Research, Elsevier, vol. 265(2), pages 559-570.
    15. Najarzadeh, Dariush, 2020. "A simple test for zero multiple correlation coefficient in high-dimensional normal data using random projection," Computational Statistics & Data Analysis, Elsevier, vol. 148(C).
    16. Kruschke, John K. & Liddell, Torrin, 2016. "The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective," OSF Preprints ksfyr, Center for Open Science.
    17. Bradley P. Carlin & James S. Hodges, 1999. "Hierarchical Proportional Hazards Regression Models for Highly Stratified Data," Biometrics, The International Biometric Society, vol. 55(4), pages 1162-1170, December.
    18. Karl Claxton & John Posnett, 1996. "An economic approach to clinical trial design and research priority‐setting," Health Economics, John Wiley & Sons, Ltd., vol. 5(6), pages 513-524, November.
    19. Chen, Z. Y. & Chou, Y. C., 2000. "Computing the noncentral beta distribution with S-system," Computational Statistics & Data Analysis, Elsevier, vol. 33(4), pages 343-360, June.
    20. Norman Simón Rodríguez Cano, 2018. "Tendencias actuales en la evaluación de políticas públicas," Ensayos de Economía 17296, Universidad Nacional de Colombia Sede Medellín.

    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:eee:csdana:v:54:y:2010:i:3:p:724-731. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.