IDEAS home Printed from https://ideas.repec.org/a/bla/istatr/v84y2016i2p181-189.html
   My bibliography  Save this article

Discussion: The Q-q Dynamic for Deeper Learning and Research

Author

Listed:
  • Xiao-Li Meng

Abstract

No abstract is available for this item.

Suggested Citation

  • Xiao-Li Meng, 2016. "Discussion: The Q-q Dynamic for Deeper Learning and Research," International Statistical Review, International Statistical Institute, vol. 84(2), pages 181-189, August.
    • Handle: RePEc:bla:istatr:v:84:y:2016:i:2:p:181-189
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/insr.12151
    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. Blitzstein, Joseph & Meng, Xiao-Li, 2010. "Nano-Project Qualifying Exam Process: An Intensified Dialogue Between Students and Faculty," The American Statistician, American Statistical Association, vol. 64(4), pages 282-290.
    2. Xiao-Li Meng & Xianchao Xie, 2014. "I Got More Data, My Model is More Refined, but My Estimator is Getting Worse! Am I Just Dumb?," Econometric Reviews, Taylor & Francis Journals, vol. 33(1-4), pages 218-250, June.
    3. Keli Liu & Xiao-Li Meng, 2014. "Comment: A Fruitful Resolution to Simpson's Paradox via Multiresolution Inference," The American Statistician, Taylor & Francis Journals, vol. 68(1), pages 17-29, 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. Iddo Gal & Irena Ograjenšek, 2016. "Rejoinder: More on Enhancing Statistics Education with Qualitative Ideas," International Statistical Review, International Statistical Institute, vol. 84(2), pages 202-209, August.

    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. Matthew Reimherr & Xiao‐Li Meng & Dan L. Nicolae, 2021. "Prior sample size extensions for assessing prior impact and prior‐likelihood discordance," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(3), pages 413-437, July.
    2. Y. Ma, 2015. "Simpson’s paradox in GDP and per capita GDP growths," Empirical Economics, Springer, vol. 49(4), pages 1301-1315, December.
    3. Guy P. Nason & Ben Powell & Duncan Elliott & Paul A. Smith, 2017. "Should we sample a time series more frequently?: decision support via multirate spectrum estimation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(2), pages 353-407, February.
    4. Josef Ditrich, 2015. "Data representativeness problem in credit scoring," Acta Oeconomica Pragensia, Prague University of Economics and Business, vol. 2015(3), pages 3-17.
    5. Iskrev, Nikolay, 2019. "On the sources of information about latent variables in DSGE models," European Economic Review, Elsevier, vol. 119(C), pages 318-332.
    6. Nikolay Iskrev, 2018. "Are asset price data informative about news shocks? A DSGE perspective," Working Papers REM 2018/33, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    7. Swamy, P.A.V.B. & Mehta, J.S. & Tavlas, G.S. & Hall, S.G., 2015. "Two applications of the random coefficient procedure: Correcting for misspecifications in a small area level model and resolving Simpson's paradox," Economic Modelling, Elsevier, vol. 45(C), pages 93-98.
    8. Aris Spanos, 2021. "Yule–Simpson’s paradox: the probabilistic versus the empirical conundrum," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(2), pages 605-635, June.
    9. Lewis, Gabriel, 2022. "Heteroskedasticity and Clustered Covariances from a Bayesian Perspective," MPRA Paper 116662, University Library of Munich, Germany.
    10. Sarah Friedrich & Gerd Antes & Sigrid Behr & Harald Binder & Werner Brannath & Florian Dumpert & Katja Ickstadt & Hans A. Kestler & Johannes Lederer & Heinz Leitgöb & Markus Pauly & Ansgar Steland & A, 2022. "Is there a role for statistics in artificial intelligence?," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(4), pages 823-846, December.

    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:istatr:v:84:y:2016:i:2:p:181-189. 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: https://edirc.repec.org/data/isiiinl.html .

    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.