Elliptical Functional Models
AbstractIn this paper, functional models with not replications are investigated within the class of the elliptical distributions. Emphasis is placed on the special case of the Student-t distribution. Main results encompasses consistency and asymptotic normality of the maximum likelihood estimators. Due to the presence of incidental parameters, standard maximum likelihood methodology cannot be used to obtain the main results, which require extensions of some existing results related to elliptical distributions. Asymptotic relative efficiencies are reported which show that the generalized least squares estimator can be highly inefficient when compared with the maximum likelihood estimator under nonnormality.
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Bibliographic InfoArticle provided by Elsevier in its journal Journal of Multivariate Analysis.
Volume (Year): 65 (1998)
Issue (Month): 1 (April)
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description
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- Kubokawa, T. & Robert, C. P., 1994. "New Perspectives on Linear Calibration," Journal of Multivariate Analysis, Elsevier, vol. 51(1), pages 178-200, October.
- Lieftinck-Koeijers, C. A. J., 1988. "Multivariate calibration: A generalization of the classical estimator," Journal of Multivariate Analysis, Elsevier, vol. 25(1), pages 31-44, April.
- Arellano-Valle, Reinaldo B. & Bolfarine, Heleno & Gasco, Loreta, 2002. "Measurement Error Models with Nonconstant Covariance Matrices," Journal of Multivariate Analysis, Elsevier, vol. 82(2), pages 395-415, August.
- Shi, Jianhong & Chen, Kun & Song, Weixing, 2014. "Robust errors-in-variables linear regression via Laplace distribution," Statistics & Probability Letters, Elsevier, vol. 84(C), pages 113-120.
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