Statistical inference for the multidimensional mixed Rasch model
Feddag, Mohand L.. (2008) Statistical inference for the multidimensional mixed Rasch model. Communications in Statistics: Simulation and Computation, Volume 37 (Number 9). pp. 1732-1749. ISSN 0361-0918Full text not available from this repository.
Official URL: http://dx.doi.org/10.1080/03610910802255832
Inference in generalized linear mixed models with multivariate random effects is often made cumbersome by the high-dimensional intractable integrals involved in the marginal likelihood. This article presents an inferential methodology based on the GEE approach. This method involves the approximations of the marginal likelihood and joint moments of the variables. It is also proposed an approximate Akaike and Bayesian information criterions based on the approximate marginal likelihood using the estimation of the parameters by the GEE approach. The different results are illustrated with a simulation study and with an analysis of real data from health-related quality of life.
|Item Type:||Journal Article|
|Subjects:||Q Science > QA Mathematics|
|Divisions:||Faculty of Science > Statistics|
|Library of Congress Subject Headings (LCSH):||Linear models (Statistics), Probabilities, Regression analysis, Multivariate analysis, Rasch models|
|Journal or Publication Title:||Communications in Statistics: Simulation and Computation|
|Publisher:||Taylor & Francis Inc.|
|Number of Pages:||18|
|Page Range:||pp. 1732-1749|
|Access rights to Published version:||Restricted or Subscription Access|
Adams, R. J., Wilson, M., Wang, W. C. (1997a). The multidimensional random coefficients
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