Inference in nonorthogonal mixed models
Corresponding Author
Department of Mathematics and Center of Mathematics and Applications, University of Beira Interior, Covilhã, Portugal
Correspondence
Dário Ferreira, Department of Mathematics and Center of Mathematics and Applications, University of Beira Interior, Covilhã, Portugal.
Email: dario@ubi.pt
Communicated by: T. E. Simos
Search for more papers by this authorDepartment of Mathematics and Center of Mathematics and Applications, University of Beira Interior, Covilhã, Portugal
Search for more papers by this authorDepartment of Mathematics and Center of Mathematics and Applications, University of Beira Interior, Covilhã, Portugal
Search for more papers by this authorCenter of Mathematics and its Applications, Faculty of Science and Technology, New University of Lisbon, Lisbon, Portugal
Search for more papers by this authorCorresponding Author
Department of Mathematics and Center of Mathematics and Applications, University of Beira Interior, Covilhã, Portugal
Correspondence
Dário Ferreira, Department of Mathematics and Center of Mathematics and Applications, University of Beira Interior, Covilhã, Portugal.
Email: dario@ubi.pt
Communicated by: T. E. Simos
Search for more papers by this authorDepartment of Mathematics and Center of Mathematics and Applications, University of Beira Interior, Covilhã, Portugal
Search for more papers by this authorDepartment of Mathematics and Center of Mathematics and Applications, University of Beira Interior, Covilhã, Portugal
Search for more papers by this authorCenter of Mathematics and its Applications, Faculty of Science and Technology, New University of Lisbon, Lisbon, Portugal
Search for more papers by this authorInstitutional Login
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Abstract
This paper presents an estimation method for the random effect parameters and the variance components in linear mixed models. These models may be orthogonal or nonorthogonal. In particular, least squares estimators and the corresponding confidence regions, based on the estimation of quantiles, are considered. As to the random effects parameters, it is only assumed that they have null mean vectors and distributions with known dispersion parameters and second order moments. So, it is not necessary that they are normally distributed. A numerical example considering the normal and the gamma distributions is included, where a comparison with the analysis of variance and a Bayesian estimation based method is provided.
