dc.creatorINFANTE, AM
dc.date1995
dc.dateJUN
dc.date2014-12-16T11:32:43Z
dc.date2015-11-26T17:51:36Z
dc.date2014-12-16T11:32:43Z
dc.date2015-11-26T17:51:36Z
dc.date.accessioned2018-03-29T00:35:00Z
dc.date.available2018-03-29T00:35:00Z
dc.identifierStatistical Papers. Springer Verlag, v. 36, n. 2, n. 183, n. 189, 1995.
dc.identifier0932-5026
dc.identifierWOS:A1995RA44000008
dc.identifier10.1007/BF02926031
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/74916
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/74916
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/74916
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1290017
dc.descriptionThe point estimation of the parameter theta of a dispersion matrix SIGMA(theta) is illustrated by considering two linear models for observations with a common scalar mean. In the first model SIGMA(theta) has the structure which assures the validity of a univariate ANOVA with repeated measures; in the second, SIGMA(theta) corresponds to a permutationally invariant distribution. In both cases, results are presented about the estimability of theta, thus obtaining the explicit form of the MINQE estimators introduced by Rao and Kleffe (1980). Finally, the consequences of the normality assumption are considered. The estimation methods based on the maximization of the complete and restricted likelihood functions are applied.
dc.description36
dc.description2
dc.description183
dc.description189
dc.languageen
dc.publisherSpringer Verlag
dc.publisherNew York
dc.relationStatistical Papers
dc.relationStat. Pap.
dc.rightsfechado
dc.rightshttp://www.springer.com/open+access/authors+rights?SGWID=0-176704-12-683201-0
dc.sourceWeb of Science
dc.subjectDISPERSION PARAMETERS
dc.subjectMINIMUM NORM QUADRATIC ESTIMATORS
dc.subjectMAXIMUM LIKELIHOOD ESTIMATOR
dc.subjectRESTRICTED MAXIMUM LIKELIHOOD ESTIMATOR
dc.subjectEQUICORRELATION MATRIX
dc.subjectHUYNH-FELDT CONDITIONS
dc.titleTHE DIFFICULTIES OF ESTIMATION OF DISPERSION PARAMETERS IN LINEAR-MODELS - AN ILLUSTRATION
dc.typeArtículos de revistas


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