Some Issues in Generalized Linear Modeling

1. Agresti, A. (2010) Analysis of ordinal categorical data (2nd ed.). London: Wiley.

   Book  Google Scholar 

2. Agresti, A. (2011) Score and pseudo score confidence intervals for categorical data analysis. Statistics in Biopharmaceutical Research, 3, 163–172.

   Article  Google Scholar 

3. Agresti, A. (2013). Categorical data analysis (3rd ed.). London: Wiley.

   MATH  Google Scholar 

4. Agresti, A. (2015). Foundations of linear and generalized linear models. London: Wiley.

   MATH  Google Scholar 

5. Agresti, A., & Coull, B. A. (1998). Approximate is better than exact for interval estimation of binomial parameters. American Statistician, 52, 119–126.

   MathSciNet  Google Scholar 

6. Agresti, A., & Kateri, M. (2016) Ordinal probability effect measures for group comparisons in multinomial cumulative link models. Biometrics, 73, 214–219.

   Article  MathSciNet  Google Scholar 

7. Albert, A., & Anderson, J. A. (1984). On the existence of maximum likelihood estimates in logistic models. Biometrika, 71, 1–10.

   Article  MathSciNet  Google Scholar 

8. Anderson, J. A. (1984). Regression and ordered categorical variables. Journal of the Royal Statistical Society, Series B, 46, 1–30.

   MathSciNet  MATH  Google Scholar 

9. Anderson, J. A., & Philips, P. R. (1981). Regression, discrimination, and measurement models for ordered categorical variables. Applied Statistics, 30, 22–31.

   Article  MathSciNet  Google Scholar 

10. Brown, L., Cai, T., & DasGupta, A. (2003). Interval estimation in exponential families. Statistica Sinica, 13, 19–49.

    MathSciNet  MATH  Google Scholar 

11. Firth, D. (1993). Bias reduction of maximum likelihood estimates. Biometrika, 80, 27–38.

    Article  MathSciNet  Google Scholar 

12. Goodman, L. A. (1979). Simple models for the analysis of association in cross-classifications having ordered categories. Journal of the American Statistical Association, 74, 537–552.

    Article  MathSciNet  Google Scholar 

13. Hedges, L. V., & Olkin, I. (1985). Statistical methods for meta-analysis. Orlando, FL: Academic Press.

    MATH  Google Scholar 

14. Hauck, W. W., & Donner, A. (1977). Wald’s test as applied to hypotheses in logit analysis. Journal of the American Statistical Association, 72, 851–853.

    MathSciNet  MATH  Google Scholar 

15. Jørgensen, B. (1987). Exponential dispersion models. Journal of the Royal Statistical Society, Series B, 49, 127–162.

    MathSciNet  MATH  Google Scholar 

16. Liang, K. Y., & Zeger, S. L. (1986). Longitudinal data analysis using generalized linear models. Biometrika, 73, 13–22.

    Article  MathSciNet  Google Scholar 

17. Lipsitz, S. R., Kim, K., & Zhao, L. (1994). Analysis of repeated categorical data using generalized estimating equations. Statistics in Medicine, 13, 1149–1163.

    Article  Google Scholar 

18. Lipsitz, S., Laird, N., & Harrington, D. (1991). Generalized estimating equations for correlated binary data: Using the odds ratio as a measure of association. Biometrika, 78, 153–160.

    Article  MathSciNet  Google Scholar 

19. Lovison, G. (2014). A note on adjusted responses, fitted values and residuals in generalized linear models. Statistical Modelling, 14, 337–359.

    Article  MathSciNet  Google Scholar 

20. McKelvey, R. D., & Zavoina, W. (1975). A statistical model for the analysis of ordinal level dependent variables. Journal of Mathematical Sociology, 4, 103–120.

    Article  MathSciNet  Google Scholar 

21. Touloumis, A., Agresti, A., & Kateri, M. (2013). GEE for multinomial responses using a local odds ratios parameterization. Biometrics, 69, 633–640.

    Article  MathSciNet  Google Scholar 

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