Interval estimation of the mean difference in the analysis of over-dispersed count data

Biometrical Journal

Volumn 55, Issue 1, 2013, Pages 114-133

  Saha, Krishna K ^a  

^a CENTRAL CONNECTICUT STATE UNIVERSITY   (United States)

Author keywords

Asymptotic confidence interval; Clustered count data; Dispersion parameter; Generalized estimating equations approach; Mean difference

Indexed keywords

INTELLIGENT SYSTEMS;

ASYMPTOTIC CONFIDENCE INTERVAL; ASYMPTOTICS; CLUSTERED COUNT DATA; CONFIDENCE INTERVAL; CONFIDENCE-INTERVAL PROCEDURES; COUNT DATUM; DISPERSION PARAMETERS; GENERALIZED ESTIMATING EQUATION APPROACH; GENERALIZED ESTIMATING EQUATIONS; MEAN DIFFERENCE;

MONTE CARLO METHODS;

EID: 84872161335     PISSN: 03233847     EISSN: 15214036     Source Type: Journal    
DOI: 10.1002/bimj.201200032     Document Type: Article

References (22)

1. Altman, D. G. (1991). Practical Statistics for Medical Research. Chapman & Hall, London.
2. Barnwal, R. K. and Paul, S. R. (1988). Analysis of one-way layout of count data with negative binomial variation. Biometrika 75, 215-222.
3. Beal, S. L. (1987). Asymptotic confidence intervals for the difference between two binomial parameters for use with small samples. Biometrics 43, 941-950.
4. Brensike, J. F., Kelsey, S. F., Passamani, E. R., Fisher, M. R., Richardson, J. M., Loh, I. K., Stone, N. J., Aldrich, R. F., Battaglini, J. W., Moriarty, D. J., Myrianthopoulos, M. B., Detre, K. M., Epstein, S. E., Levy, R. I. (1982). National Heart, Lung, and Blood Institute Type II coronary intervention study: design, methods, and baseline characteristics. Controlled Clinical Trials 3, 91-111.
5. Brown, B. M. (1982). Robustness against inequality of variances. Australian Journal of Statistics 24, 283-295.
6. Cochran, W. G. (1977). Sampling Techniques (3rd edn.). Wiley, New York, pp. 30-67.
7. Dean, C. B., Lawless, J. F. and Willmot, G. E. (1989). A mixed Poisson-inverse-Gaussian regression model. Canadian Journal of Statistics 17, 171-181.
8. Hinde, J. (1982). Compound Poisson regression model. In: Gilchrist, R. (Ed.), in GLIM82: Proceedings of the International Conference on Generalized Linear Model, 109-121. Berlin: Springer.
9. Lindsey, J. K. (1995). Modelling Frequency and Count Data. Oxford University Press, Oxford.
10. Newcombe, R. G. (1998). Interval estimation for the difference between independent proportions: comparison of eleven methods. Statistics in Medicine 17, 873-890.
11. Newcombe, R. G. (2011a). Measures of location for confidence intervals for proportions. Communications in Statistics-Theory & Methods 40, 1743-1767.
12. Newcombe, R. G. (2011b). Propagating imprecision: combining confidence intervals from independent sources. Communications in Statistics-Theory & Methods 40, 3154-3180.
13. Newcombe, R. G. (2012). Confidence Intervals for Proportions and Related Measures of Effect Size. Chapman & Hall/CRC Biostatistics Series, Taylor & Francis, Cardiff University, Wales.
14. Rao, J. N. K. and Scott, A. J. (1992). A simple method for the analysis of clustered binary data. Biometrics 48, 577-585.
15. Rao, J. N. K. and Scott, A. J. (1999). A simple method for analysing overdispersion in clustered Poisson data. Statistics in Medicine 18, 1373-1385.
16. Saha, K. K. (2008). Analysis of one-way layout of count data in the presence of over or under dispersion. Journal of Statistical Planning and Inference 138, 2067-2081.
17. Saha, K. K. and Bilisoly, R. (2009). Testing the homogeneity of several groups of count data in the presence of unequal dispersions. Computational Statistics and Data Analysis 53, 3305-3313.
18. Saha, K. K. and Paul, S. R. (2005). Bias-corrected maximum likelihood estimator of the negative binomial dispersion parameter. Biometrics 61, 179-185.
19. Scott, A. J. and Wu, C. F. J. (1981). On the asymptotic distribution of ratio and regression estimators. Journal of the American Statistical Association 76, 98-102.
20. See, K. and Bailer, A. J. (1998). Added risk and inverse estimation for count responses in reproductive aquatic toxicology studies. Biometrics 54, 67-73.
21. Zeger, S. L. and Liang, K. Y. (1986). Longitudinal data analysis for discrete and continuous outcomes. Biometrics 42, 121-130.
22. Zou, G. Y. (2008). On the estimation of additive interaction by use of the four-by-two table and beyond. American Journal of Epidemiology 168, 212-224.