IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v67y2011i3p1073-1082.html
   My bibliography  Save this article

A Beta-Mixture Model for Assessing Genetic Population Structure

Author

Listed:
  • Rongwei Fu
  • Dipak K. Dey
  • Kent E. Holsinger

Abstract

No abstract is available for this item.

Suggested Citation

  • Rongwei Fu & Dipak K. Dey & Kent E. Holsinger, 2011. "A Beta-Mixture Model for Assessing Genetic Population Structure," Biometrics, The International Biometric Society, vol. 67(3), pages 1073-1082, September.
    • Handle: RePEc:bla:biomet:v:67:y:2011:i:3:p:1073-1082
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2010.01506.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. S. P. Brooks & P. Giudici & G. O. Roberts, 2003. "Efficient construction of reversible jump Markov chain Monte Carlo proposal distributions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 3-39, January.
    2. George Nicholson & Albert V. Smith & Frosti Jónsson & Ómar Gústafsson & Kári Stefánsson & Peter Donnelly, 2002. "Assessing population differentiation and isolation from single‐nucleotide polymorphism data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 695-715, October.
    Full references (including those not matched with items on IDEAS)

    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. Sridhar Narayanan, 2013. "Bayesian estimation of discrete games of complete information," Quantitative Marketing and Economics (QME), Springer, vol. 11(1), pages 39-81, March.
    2. Riccardo (Jack) Lucchetti & Luca Pedini, 2020. "ParMA: Parallelised Bayesian Model Averaging for Generalised Linear Models," Working Papers 2020:28, Department of Economics, University of Venice "Ca' Foscari".
    3. Azari Soufiani, Hossein & Diao, Hansheng & Lai, Zhenyu & Parkes, David C., 2013. "Generalized Random Utility Models with Multiple Types," Scholarly Articles 12363923, Harvard University Department of Economics.
    4. Griffin, J.E. & Steel, M.F.J., 2010. "Bayesian inference with stochastic volatility models using continuous superpositions of non-Gaussian Ornstein-Uhlenbeck processes," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2594-2608, November.
    5. Chen, Langnan & Luo, Jiawen & Liu, Hao, 2013. "The determinants of liquidity with G-RJMCMC-VS model: Evidence from China," Economic Modelling, Elsevier, vol. 35(C), pages 192-198.
    6. David I. Hastie & Peter J. Green, 2012. "Model choice using reversible jump Markov chain Monte Carlo," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 66(3), pages 309-338, August.
    7. Mathieu Gautier & Toby Dylan Hocking & Jean-Louis Foulley, 2010. "A Bayesian Outlier Criterion to Detect SNPs under Selection in Large Data Sets," PLOS ONE, Public Library of Science, vol. 5(8), pages 1-16, August.
    8. Liqun Wang & James Fu, 2007. "A practical sampling approach for a Bayesian mixture model with unknown number of components," Statistical Papers, Springer, vol. 48(4), pages 631-653, October.
    9. Streftaris, George & Worton, Bruce J., 2008. "Efficient and accurate approximate Bayesian inference with an application to insurance data," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2604-2622, January.
    10. Helen Armstrong & Christopher K. Carter & Kevin K. F. Wong & Robert Kohn, 2007. "Bayesian Covariance Matrix Estimation using a Mixture of Decomposable Graphical Models," Discussion Papers 2007-13, School of Economics, The University of New South Wales.
    11. Soraggi, Samuele & Wiuf, Carsten, 2019. "General theory for stochastic admixture graphs and F-statistics," Theoretical Population Biology, Elsevier, vol. 125(C), pages 56-66.
    12. Tsung-I Lin & Hsiu Ho & Pao Shen, 2009. "Computationally efficient learning of multivariate t mixture models with missing information," Computational Statistics, Springer, vol. 24(3), pages 375-392, August.
    13. Yinghui Wei & Peter Neal & Sandra Telfer & Mike Begon, 2012. "Statistical analysis of an endemic disease from a capture--recapture experiment," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(12), pages 2759-2773, August.
    14. Nick Patterson & Alkes L Price & David Reich, 2006. "Population Structure and Eigenanalysis," PLOS Genetics, Public Library of Science, vol. 2(12), pages 1-20, December.
    15. Davison, D. & Pritchard, J.K. & Coop, G., 2009. "An approximate likelihood for genetic data under a model with recombination and population splitting," Theoretical Population Biology, Elsevier, vol. 75(4), pages 331-345.
    16. McVinish, R. & Mengersen, K., 2008. "Semiparametric Bayesian circular statistics," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4722-4730, June.
    17. Meyer-Gohde, Alexander & Neuhoff, Daniel, 2015. "Generalized exogenous processes in DSGE: A Bayesian approach," SFB 649 Discussion Papers 2015-014, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    18. Chigozie E. Utazi, 2017. "Bayesian Single Changepoint Estimation in a Parameter-driven Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(3), pages 765-779, September.
    19. Gagnon, Philippe & Bédard, Mylène & Desgagné, Alain, 2019. "Weak convergence and optimal tuning of the reversible jump algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 161(C), pages 32-51.
    20. Alejandro Ochoa & John D Storey, 2021. "Estimating FST and kinship for arbitrary population structures," PLOS Genetics, Public Library of Science, vol. 17(1), pages 1-36, January.

    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:67:y:2011:i:3:p:1073-1082. 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.