Estimation of mean form and mean form difference under elliptical laws

José A. Díaz-García, Francisco J. Caro-Lopera

Resultado de la investigación: Contribución a una revistaArtículoInvestigaciónrevisión exhaustiva

Resumen

© 2017, Institute of Mathematical Statistics. All rights reserved. The matrix variate elliptical generalization of [30] is presented in this work. The published Gaussian case is revised and modified. Then, new aspects of identifiability and consistent estimation of mean form and mean form difference are considered under elliptical laws. For example, instead of using the Euclidean distance matrix for the consistent estimates, exact formulae are derived for the moments of the matrix B = Xc(Xc)T; where Xcis the centered landmark matrix. Finally, a complete application in Biology is provided; it includes estimation, model selection and hypothesis testing.
Idioma originalInglés estadounidense
Páginas (desde-hasta)2424-2460
Número de páginas37
PublicaciónElectronic Journal of Statistics
DOI
EstadoPublicada - 1 ene 2017

Huella dactilar

Euclidean Distance Matrix
Consistent Estimation
Consistent Estimates
Identifiability
Landmarks
Hypothesis Testing
Model Selection
Biology
Moment
Statistics
Form
Generalization
Euclidean distance
Model selection
Hypothesis testing

Citar esto

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Estimation of mean form and mean form difference under elliptical laws. / Díaz-García, José A.; Caro-Lopera, Francisco J.

En: Electronic Journal of Statistics, 01.01.2017, p. 2424-2460.

Resultado de la investigación: Contribución a una revistaArtículoInvestigaciónrevisión exhaustiva

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AB - © 2017, Institute of Mathematical Statistics. All rights reserved. The matrix variate elliptical generalization of [30] is presented in this work. The published Gaussian case is revised and modified. Then, new aspects of identifiability and consistent estimation of mean form and mean form difference are considered under elliptical laws. For example, instead of using the Euclidean distance matrix for the consistent estimates, exact formulae are derived for the moments of the matrix B = Xc(Xc)T; where Xcis the centered landmark matrix. Finally, a complete application in Biology is provided; it includes estimation, model selection and hypothesis testing.

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