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Revista Colombiana de Estadística

Print version ISSN 0120-1751

Rev.Colomb.Estad. vol.33 no.2 Bogotá July/Dec. 2010

 

Procedimiento y algoritmo de estimación en modelos multinivel para proporciones

Procedure and Estimation Algorithm in Multilevel Models for Proportions

ERNESTINA CASTELLS1, MARIO M. OJEDA2, MINERVA MONTERO3

1Universidad Autónoma de Guerrero, Facultad de Matemática, Acapulco, México. Profesora titular. Email: ernestinacg@yahoo.com
2Universidad Veracruzana, Facultad de Estadística e Informática, Xalapa, México. Profesor titular. Email: mojeda@uv.mx
3Instituto de Cibernética, Matemática y Física, Departamento de Matemática, La Habana, Cuba. Investigadora auxiliar. Email: minerva@icmf.inf.cu


Resumen

En este artículo se describe un procedimiento para la estimación de parámetros fijos y aleatorios en modelos multinivel para proporciones. El procedimiento de estimación se basa en el método de los mínimos cuadrados generalizados. Una vez que se formula el modelo, se demuestra que es posible aplicar la teoría asintótica de estimación en el marco del modelo lineal general. Se elabora un algoritmo que permite calcular los estimadores propuestos. La aplicación se ilustra con un ejemplo de meta-análisis. Se concluye que el procedimiento presentado puede ser una estrategia favorable en investigaciones aplicadas.

Palabras clave: mínimos cuadrados generalizados iterativos, modelos multinivel, tablas de contingencia.


Abstract

This paper describes a procedure for the estimation of fixed and random parameters in multilevel model for proportions. The estimation procedure is developed using Iterative Generalized Least Squares. Once the model is formulated, we demonstrate that it is possible to apply the asymptotic estimation theory in the framework of the general lineal model. An algorithm to calculate the proposed estimators is elaborated. We illustrate the application using an example of meta-analysis. It is concluded that the proposed procedure can be favorable strategy to do applied research.

Key words: Contingency tables, Iterative generalized least squares, Multilevel models.


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Referencias

1. Abrams, K. & Sansó, B. (1998), 'Approximate Bayesian Inference for Random Effects meta-analysis', Statistics in Medicine 17, 201-218.         [ Links ]

2. Agresti, A. (2002), Categorical Data Analysis, Wiley, New York.         [ Links ]

3. Anderson, T. W. (1958), An Introduction to Multivariate Statistical Analysis, Wiley, New York.         [ Links ]

4. Breslow, N. E. & Zhao, L. P. (1988), 'Logistic Regression for Stratified Case-Control Studies', Biometrics 44, 891-899.         [ Links ]

5. Brik, A. S. & Raudenbush, S. W. (1992), Hierarchical Linear Models: Applications and Data Analysis Methods, Sage Publications, California.         [ Links ]

6. Castells, E. (1985), Estimación en un modelo con parámetros aleatorios, Tesis de Maestría, Facultad de Matemática, Universidad de La Habana, La Habana.         [ Links ]

7. Efron, B. (1996), 'Empirical Bayes Methods for Combining Likelihoods', Journal of the American Statistical Association 96(434), 538-565.         [ Links ]

8. Fears, T. R. & Brown, C. C. (1986), 'Logistic Regression Methods for Retrospective Case-Control Dtudies using Complex Sampling Procedures', Biometrics 42, 955-960.         [ Links ]

9. Forthofer, R. N. & Lehnen, R. G. (1981), Public Program Analysis: A New Categorical Data Approach, Lifetime Learning Publications, Belmont, California.         [ Links ]

10. Forthoper, R. N. & Koch, G. G. (1973), 'An Analysis for Compounded Functions of Categorical Data', Biometrics 29, 143-157.         [ Links ]

11. Gilks, W. R., Thomas, A. & Spiegelhalter, D. J. (1994), 'A language and Program for Complex Bayesian Modelling', Statistician 43, 169-177.         [ Links ]

12. Glass, G. V. (1976), 'Primary, Secondary and Meta-Analysis of Research', Educational Researcher 5, 3-8.         [ Links ]

13. Goldstein, H. (1987), Multilevel Models in Educational and Social Research, Charles Griffin, London.         [ Links ]

14. Goldstein, H. (1995), Multilevel Statistical Models, 2 edn, Halsted Press, New York.         [ Links ]

15. Goldstein, H. & Rasbash, J. (1996), 'Improved Approximations for Multilevel Models with Binary Responses', Journal of the Royal Statistical Society. Series A(57), 395-407.         [ Links ]

16. Goldstein, H., Rasbash, J., Plewis, I., Draper, D., Browne, W., Yang, M., Woodhouse, G. & MJR, H. (1998), A user's guide to MLwiN, Institute of Education.         [ Links ]

17. Grizzle, J. E., Starmer, C. F. & Koch, G. G. (1969), 'Analysis of Categorical Data by Linear Models', Biometrics 25, 489-504.         [ Links ]

18. Hamerle, A. & Honning, G. (1995), Panel analysis for qualitative variables, 'A Handbook for Statistical Modeling in the Social and Behavioral Sciences', Plenum, New York, p. 401-451.         [ Links ]

19. Hartzel, J., Liu, I-M, & Agresti, A. (2001), 'Describing Heterogeneous Effects in Stratified Ordinal Contingency Tables, with Application to Multi-CCenter clinical trials', Computational Statistics and Data Analysis 35, 429-499.         [ Links ]

20. Hedges, L. V. & Olkin, I. (1985), Statistical Methods for Meta-analysis, Academic Press, New York.         [ Links ]

21. Hsiao, C. (1995), Analysis of Panel Data, Cambridge University Press, New York.         [ Links ]

22. Kleffe, J. (1976), 'A Note on MINQUE for Normal Models', Mathematische Operationsforschung und Statistik 7, 107-114.         [ Links ]

23. Kuk, A. Y. C. (1995), 'Asymtotically Unbiased Estimation in Generalized Linear Models with Random Effects', Journal of the Royal Statistical Socity 57, 395-407.         [ Links ]

24. Lee, Y. & Nelder, J. A. (2002), 'Analysis of Ulcer data Using Hierarchical Generalized Linear Models', Statistics in Medicine 21, 191-202.         [ Links ]

25. Longford, N. (1995), Random coefficient models: Handbook of Statistiscal Modeling for the Social and Behavioral Sciences, Plenum Press, New York.         [ Links ]

26. Lubin, J. H., Blot, W. J., Berrino, F., Flamant, R., Gillis, C. R., Kunze, M., Schmäwhl, D. & Visco, G. (1984), 'Patterns of Lung Cancer Risg According to Type of Cigarrette Smoked', International Journal of Cancer 33, 569-576.         [ Links ]

27. Montero, M. (2006), Análisis de tablas de contingencia: un enfoque multinivel, Tesis de Doctorado, Facultad de Matemática, Universidad de La Habana, La Habana.         [ Links ]

28. Montero, M., Castell, E. & Ojeda, M. M. (2007), 'Fitting a Multilevel Model to a Sample of Contingency Tables using the GSK Approach', Revista Investigación Operacional 28(3), 204-214.         [ Links ]

29. Montero, M., Castell, E. & Ojeda, M. M. (2008), 'Analysis of a Contingency Tables sample: A Simulation Study', Revista Ciencias Matemáticas 24, 83-92.         [ Links ]

30. Montero, M. & Guerra, V. (2005), 'Estimating Multilevel Models for Categorical Data via Generalized Least Squares', Revista Colombiana de Estadística. 21(8), 63-76.         [ Links ]

31. Rao, C. R. (1973), Linear Statistical Inference and Its Applications, Segunda edn, John Wiley & Sons, Inc., New York.         [ Links ]

32. Rudas, T. (1986), 'A Monte Carlo Comparison of the Small Sample Behavior of the Pearson, the Likelihood Ratio and the Cressie-Read Statistics', Journal of Statistical Computation and Simulation(24), 107-120.         [ Links ]

33. Schmidt, T. C., Spiegelhalter, D. J. & Thomas, A. (1995), 'Bayesian Approaches to Random'effects Meta-analysis: A Comparative Study', Statistics in Medicine 14, 2685-2699.         [ Links ]

34. Snijders, T. A. B. & Bosker, R. (1999), Introduction to Basic and Advanced Multilevel Modelling, Sage, London.         [ Links ]

35. Turner, R. .., Omar, R. Z., Yang, M., Goldstein, H. & Thompson, S. G. (2000), 'Multilevel models for meta-analysis of clinical trials with binary outcomes', Statistics in Medicine 19, 3417-3432.         [ Links ]

[Recibido en abril de 2009. Aceptado en octubre de 2010]

Este artículo se puede citar en LaTeX utilizando la siguiente referencia bibliográfica de BibTeX:

@ARTICLE{RCEv33n2a04,
    AUTHOR  = {Castells, Ernestina and Ojeda, Mario M. and Montero, Minerva},
    TITLE   = {{Procedimiento y algoritmo de estimación en modelos multinivel para proporciones}},
    JOURNAL = {Revista Colombiana de Estadística},
    YEAR    = {2010},
    volume  = {33},
    number  = {2},
    pages   = {233-250}
}

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