Maximum likelihood estimation of generalised linear models for multivariate normal covariance matrix

Biometrika

Volumn 87, Issue 2, 2000, Pages 425-435

  Pourahmadi, Mohsen ^a  

^a NORTHERN ILLINOIS UNIVERSITY   (United States)

Author keywords

Asymptotic normality; Cholesky decomposition; Fisher information; Newton Raphson algorithm; Unconstrained parameterisation; Variable selection and diagnostics

Indexed keywords

EID: 0001304198     PISSN: 00063444     EISSN: None     Source Type: Journal    
DOI: 10.1093/biomet/87.2.425     Document Type: Article

References (15)

1. BERGER, O. J., LISEO, B. & WOLPERT, R. (1999). Integrated likelihood methods for eliminating nuisance parameters (with Discussion). Statist. Sci. 14, 1-28.
2. BROWN, P. J., LE, N. D. & ZIDEK, J. V. (1994). Inference for a covariance matrix. In Aspects of Uncertainty, Ed. P. R. Freeman and A. F. M. Smith, pp. 77-90. Chichester: John Wiley.
3. CHIU, T. Y. M., LEONARD, T. & TSUI, K. W. (1996). The matrix-logarithm covariance model. J. Am. Statist. Assoc. 91, 198-210.
4. COX, D. R. & WERMUTH, N. (1996). Multivariate Dependencies: Models, Analysis and Interpretation. London: Chapman and Hall.
5. DIGGLE, P. J., LIANG, K. Y. & ZEGER, S. L. (1994). Analysis of Longitudinal Data. Oxford: Oxford University Press.
6. KENWARD, M. G. (1987). A method for comparing profiles of repeated measurements. Appl. Statist. 36, 296-308.
7. LEONARD, T. (1982). Comments on 'A simple predictive density function' by M. Lejeune and G. D. Faulkenberry. J. Am. Statist. Assoc. 77, 657-8.
8. LEONARD, T. & HSU, J. S. J. (1992). Bayesian inference for a covariance matrix. Ann. Statist. 20, 1669-96.
9. MCCULLAGH, P. & NELDER, J. A. (1989). Generalized Linear Models, 2nd ed. London: Chapman and Hall.
10. PINHEIRO, J. D. & BATES, D. M. (1996). Unconstrained parametrizations for variance-covariance matrices. Statist. Comp. 6, 289-96.
11. POURAHMADI, M. (1999). Joint mean-covariance models with applications to longitudinal data: Unconstrained parameterisation. Biometrika 86, 677-90.
12. SMYTH, G. K. (1989). Generalized linear models with varying dispersion. J. R Statist. Soc. B 51, 47-60.
13. SPECTER, P. (1994). An Introduction to S and S-Plus. Belmont, CA: Duxbury.
14. VERBYLA, A. P. (1993). Modelling variance heterogeneity: Residual maximum likelihood and diagnostics. J. R. Statist. Soc. B 55, 493-508.
15. ZIMMERMAN, D. L. & NÚÑEZ-ANTÓN, V. (1997). Structured antedependence models for longitudinal data. In Modeling Longitudinal and Spatially Correlated Data: Methods. Applications, and Future Directions, Springer Lecture Notes in Statistics, No. 122, Ed. T. G. Gregoire et al., pp. 63-76. New York: Springer-Verlag.