IDEAS home Printed from https://ideas.repec.org/a/bla/scjsta/v43y2016i1p191-201.html
   My bibliography  Save this article

Integrated Likelihood Inference in Small Sample Meta-analysis for Continuous Outcomes

Author

Listed:
  • Ruggero Bellio
  • Annamaria Guolo

Abstract

No abstract is available for this item.

Suggested Citation

  • Ruggero Bellio & Annamaria Guolo, 2016. "Integrated Likelihood Inference in Small Sample Meta-analysis for Continuous Outcomes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(1), pages 191-201, March.
    • Handle: RePEc:bla:scjsta:v:43:y:2016:i:1:p:191-201
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/sjos.12172
    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. Colin J. Roberts, 2005. "Issues in Meta‐Regression Analysis: An Overview," Journal of Economic Surveys, Wiley Blackwell, vol. 19(3), pages 295-298, July.
    2. Thomas A. Severini, 2007. "Integrated likelihood functions for non-Bayesian inference," Biometrika, Biometrika Trust, vol. 94(3), pages 529-542.
    3. Mark G. Vangel & Andrew L. Rukhin, 1999. "Maximum Likelihood Analysis for Heteroscedastic One-Way Random Effects ANOVA in Interlaboratory Studies," Biometrics, The International Biometric Society, vol. 55(1), pages 129-136, March.
    4. T. A. Severini, 2010. "Likelihood ratio statistics based on an integrated likelihood," Biometrika, Biometrika Trust, vol. 97(2), pages 481-496.
    5. N. Sartori, 2003. "Modified profile likelihoods in models with stratum nuisance parameters," Biometrika, Biometrika Trust, vol. 90(3), pages 533-549, September.
    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. Giuliana Cortese & Nicola Sartori, 2016. "Integrated likelihoods in parametric survival models for highly clustered censored data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(3), pages 382-404, July.
    2. Pakel, Cavit, 2019. "Bias reduction in nonlinear and dynamic panels in the presence of cross-section dependence," Journal of Econometrics, Elsevier, vol. 213(2), pages 459-492.
    3. H. V. Kulkarni & S. M. Patil, 2021. "Uniformly implementable small sample integrated likelihood ratio test for one-way and two-way ANOVA under heteroscedasticity and normality," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(2), pages 273-305, June.
    4. Thomas A. Severini, 2023. "Integrated likelihood inference in multinomial distributions," METRON, Springer;Sapienza Università di Roma, vol. 81(2), pages 131-142, August.
    5. Schumann, Martin & Severini, Thomas A. & Tripathi, Gautam, 2023. "The role of score and information bias in panel data likelihoods," Journal of Econometrics, Elsevier, vol. 235(2), pages 1215-1238.
    6. Zhenyu Zhao & Thomas A. Severini, 2017. "Integrated likelihood computation methods," Computational Statistics, Springer, vol. 32(1), pages 281-313, March.
    7. Luigi Pace & Alessandra Salvan & Laura Ventura, 2011. "Adjustments of profile likelihood through predictive densities," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 63(5), pages 923-937, October.
    8. De Bin, Riccardo, 2016. "On the equivalence between conditional and random-effects likelihoods in exponential families," Statistics & Probability Letters, Elsevier, vol. 110(C), pages 34-38.
    9. Zaigraev, A. & Podraza-Karakulska, A., 2014. "Maximum integrated likelihood estimator of the interest parameter when the nuisance parameter is location or scale," Statistics & Probability Letters, Elsevier, vol. 88(C), pages 99-106.
    10. Sartori, N. & Severini, T.A. & Marras, E., 2010. "An alternative specification of generalized linear mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 575-584, February.
    11. repec:hal:spmain:info:hdl:2441/dambferfb7dfprc9m052g20qh is not listed on IDEAS
    12. Cavit Pakel & Neil Shephard & Kevin Sheppard, 2009. "Nuisance parameters, composite likelihoods and a panel of GARCH models," Economics Papers 2009-W12, Economics Group, Nuffield College, University of Oxford.
    13. Cavit Pakel & Neil Shephard & Kevin Sheppard & Robert F. Engle, 2021. "Fitting Vast Dimensional Time-Varying Covariance Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(3), pages 652-668, July.
    14. Julian P. T. Higgins & Simon G. Thompson & David J. Spiegelhalter, 2009. "A re‐evaluation of random‐effects meta‐analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 137-159, January.
    15. Geert Dhaene & Koen Jochmans, 2015. "Split-panel Jackknife Estimation of Fixed-effect Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(3), pages 991-1030.
    16. Wolgang Karl Härdle & Li-Shan Huang, 2013. "Analysis of Deviance in Generalized Partial Linear Models," SFB 649 Discussion Papers SFB649DP2013-028, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    17. Dhaene, Geert & Jochmans, Koen, 2016. "Likelihood Inference In An Autoregression With Fixed Effects," Econometric Theory, Cambridge University Press, vol. 32(5), pages 1178-1215, October.
    18. Joan Costa‐Font & Marin Gemmill & Gloria Rubert, 2011. "Biases in the healthcare luxury good hypothesis?: a meta‐regression analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(1), pages 95-107, January.
    19. Schumann, Martin & Severini, Thomas A. & Tripathi, Gautam, 2021. "Integrated likelihood based inference for nonlinear panel data models with unobserved effects," Journal of Econometrics, Elsevier, vol. 223(1), pages 73-95.
    20. Johannes S. Kunz & Kevin E. Staub & Rainer Winkelmann, 2021. "Predicting individual effects in fixed effects panel probit models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(3), pages 1109-1145, July.
    21. Germà Bel & Xavier Fageda, 2009. "Factors explaining local privatization: a meta-regression analysis," Public Choice, Springer, vol. 139(1), pages 105-119, April.

    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:scjsta:v:43:y:2016:i:1:p:191-201. 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=0303-6898 .

    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.