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Two Bayesian/frequentist challenges for categorical data analyses

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  • Alan Agresti

Abstract

We discuss two challenging scenarios for frequentist and/or Bayesian inference for categorical data. First, for parameter space regions described by order restrictions, frequentist methods are less straightforward than Bayesian methods, especially for interval estimation. Second, for marginal modeling, frequentist inference is currently feasible only in relatively simplistic settings and Bayesian solutions seem to be essentially non-existent. Both areas have substantial scope for future research. Copyright Sapienza Università di Roma 2014

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  • Alan Agresti, 2014. "Two Bayesian/frequentist challenges for categorical data analyses," METRON, Springer;Sapienza Università di Roma, vol. 72(2), pages 125-132, August.
    • Handle: RePEc:spr:metron:v:72:y:2014:i:2:p:125-132
    DOI: 10.1007/s40300-014-0036-1
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    References listed on IDEAS

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    1. Anestis Touloumis & Alan Agresti & Maria Kateri, 2013. "GEE for Multinomial Responses Using a Local Odds Ratios Parameterization," Biometrics, The International Biometric Society, vol. 69(3), pages 633-640, September.
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    3. Bartolucci, Francesco & Scaccia, Luisa & Farcomeni, Alessio, 2012. "Bayesian inference through encompassing priors and importance sampling for a class of marginal models for categorical data," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 4067-4080.
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    6. Bartolucci F. & Forcina A. & Dardanoni V., 2001. "Positive Quadrant Dependence and Marginal Modeling in Two-Way Tables With Ordered Margins," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1497-1505, December.
    7. Wolak, Frank A., 1989. "Local and Global Testing of Linear and Nonlinear Inequality Constraints in Nonlinear Econometric Models," Econometric Theory, Cambridge University Press, vol. 5(1), pages 1-35, April.
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