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Ordinal probability effect measures for group comparisons in multinomial cumulative link models

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  • Alan Agresti
  • Maria Kateri

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  • Alan Agresti & Maria Kateri, 2017. "Ordinal probability effect measures for group comparisons in multinomial cumulative link models," Biometrics, The International Biometric Society, vol. 73(1), pages 214-219, March.
    • Handle: RePEc:bla:biomet:v:73:y:2017:i:1:p:214-219
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    File URL: http://hdl.handle.net/10.1111/biom.12565
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    References listed on IDEAS

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    1. Kelley, Ken, 2007. "Confidence Intervals for Standardized Effect Sizes: Theory, Application, and Implementation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 20(i08).
    2. Olivier Thas & Jan De Neve & Lieven Clement & Jean-Pierre Ottoy, 2012. "Probabilistic index models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 74(4), pages 623-671, September.
    3. Tong-Yu Lu & Wai-Yin Poon & Siu Cheung, 2014. "A Unified Framework for the Comparison of Treatments with Ordinal Responses," Psychometrika, Springer;The Psychometric Society, vol. 79(4), pages 605-620, October.
    4. Jing Cheng, 2009. "Estimation and Inference for the Causal Effect of Receiving Treatment on a Multinomial Outcome," Biometrics, The International Biometric Society, vol. 65(1), pages 96-103, March.
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    Cited by:

    1. Jean‐Sauveur Ay, 2021. "The Informational Content of Geographical Indications," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(2), pages 523-542, March.
    2. Ay, Jean-Sauveur & Le Gallo, Julie, 2021. "The Signaling Values of Nested Wine Names," Working Papers 321851, American Association of Wine Economists.
    3. Lu, Jiannan, 2018. "On the partial identification of a new causal measure for ordinal outcomes," Statistics & Probability Letters, Elsevier, vol. 137(C), pages 1-7.
    4. Domenico Piccolo & Rosaria Simone, 2019. "The class of cub models: statistical foundations, inferential issues and empirical evidence," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(3), pages 389-435, September.
    5. Maria Iannario & Anna Clara Monti, 2022. "Modelling consumer perceptions of service quality for urban public transport systems using statistical models for ordinal data," METRON, Springer;Sapienza Università di Roma, vol. 80(1), pages 61-76, April.
    6. Jiannan Lu & Peng Ding & Tirthankar Dasgupta, 2018. "Treatment Effects on Ordinal Outcomes: Causal Estimands and Sharp Bounds," Journal of Educational and Behavioral Statistics, , vol. 43(5), pages 540-567, October.
    7. Alan Agresti & Claudia Tarantola, 2018. "Simple ways to interpret effects in modeling ordinal categorical data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(3), pages 210-223, August.
    8. Benjamin R. Shear & Sean F. Reardon, 2021. "Using Pooled Heteroskedastic Ordered Probit Models to Improve Small-Sample Estimates of Latent Test Score Distributions," Journal of Educational and Behavioral Statistics, , vol. 46(1), pages 3-33, February.
    9. Alan Agresti & Maria Kateri, 2019. "The class of CUB models: statistical foundations, inferential issues and empirical evidence," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(3), pages 445-449, September.
    10. Jiannan Lu & Yunshu Zhang & Peng Ding, 2020. "Sharp bounds on the relative treatment effect for ordinal outcomes," Biometrics, The International Biometric Society, vol. 76(2), pages 664-669, June.

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