{"id":85143,"date":"2018-03-10T00:09:33","date_gmt":"2018-03-10T00:09:33","guid":{"rendered":"https:\/\/www.deberes.net\/tesis\/sin-categoria\/aportaciones-a-la-teoria-de-distribuciones-elipticas-bivariantes\/"},"modified":"2018-03-10T00:09:33","modified_gmt":"2018-03-10T00:09:33","slug":"aportaciones-a-la-teoria-de-distribuciones-elipticas-bivariantes","status":"publish","type":"post","link":"https:\/\/www.deberes.net\/tesis\/matematicas\/aportaciones-a-la-teoria-de-distribuciones-elipticas-bivariantes\/","title":{"rendered":"Aportaciones a la teoria de distribuciones elipticas bivariantes"},"content":{"rendered":"<h2>Tesis doctoral de <strong>  Jimenez Lopez Jos\u00e9 Domingo <\/strong><\/h2>\n<p>En esta memoria se estudian tres subfamilias de distribuciones el\u00edpticas bivariantes, concretamente las distribuciones pearson tipo vii, pearsontipo ii y la distribuci\u00f3n tipo kotz. En primer lugar, partiendo de la caracterizaci\u00f3n de las mismas a pjartir de su funci\u00f3n de densidad, se presentan sus principales proiedades: funci\u00f3n caracter\u00edsticas, distribuciones marginales y condicionadas y momentos de primer y segundo orden.  seguidadmente, se analizan algunos problemas de inferencias en cada una de estas distribuciones. En una jprimera etapa, se aborda el problema de estimaci\u00f3nde los par\u00e1metros de estas distribuciones utilizando algunos m\u00e9todos usuales de estimaci\u00f3npuntual (m\u00e9todo de los momentos y m\u00e9todo de ma\u00c2\u00bfscima verosimilitud) bajo la hip\u00f3tesis habitual de independencia entre los vectores aleatoriso de la muestra y considerando, por otra parte,una dependencia matricial conjunta entre dichos vectores. A continuaci\u00f3n, se analiza la propeidad de eficiencia de los estimadores de los par\u00e1metros de las distribuciones antes mencionadas bajo la hip\u00f3tesis de dependencia matricial impuesta a la muestra. En primer lugar, generalizando lo que ocurre en el caso normal, se contruyen estimadores insesgados y se demuestra que estos estimadores no son eficiente. Por este motivo, se considera la familia de estimadores insesgados de los par\u00e1metros obtenidos mediante combinaciones lineales de los vectores aleatorios bidimiensionales de la muestra y de las submatrices de la mtriz de cuasivarianza muestral, y se demuestra que no es posible encontrar estimadores eficientes dentro de esta familia. No obstante, conluimos que los estimadores de esta familia que minimizan los determiantes de las matrices de covarianzas son an\u00e1logos a los obtenidos en el caso normal, concretamente, la media muestral y la matriz de cuasivarianza muestral, afectada por la constante de insesgadez.<\/p>\n<p>&nbsp;<\/p>\n<h3>Datos acad\u00e9micos de la tesis doctoral \u00ab<strong>Aportaciones a la teoria de distribuciones elipticas bivariantes<\/strong>\u00ab<\/h3>\n<ul>\n<li><strong>T\u00edtulo de la tesis:<\/strong>\u00a0 Aportaciones a la teoria de distribuciones elipticas bivariantes <\/li>\n<li><strong>Autor:<\/strong>\u00a0  Jimenez Lopez Jos\u00e9 Domingo <\/li>\n<li><strong>Universidad:<\/strong>\u00a0 Granada<\/li>\n<li><strong>Fecha de lectura de la tesis:<\/strong>\u00a0 20\/06\/2000<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3>Direcci\u00f3n y tribunal<\/h3>\n<ul>\n<li><strong>Director de la tesis<\/strong>\n<ul>\n<li>Ram\u00f3n Guti\u00e9rrez Jaimez<\/li>\n<\/ul>\n<\/li>\n<li><strong>Tribunal<\/strong>\n<ul>\n<li>Presidente del tribunal: Antonio Pascual acosta <\/li>\n<li>joaquin Mu\u00f1oz garcia (vocal)<\/li>\n<li>josefa Linares perez (vocal)<\/li>\n<li>Juan  Carlos Ru\u00edz molina (vocal)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Tesis doctoral de Jimenez Lopez Jos\u00e9 Domingo En esta memoria se estudian tres subfamilias de distribuciones el\u00edpticas bivariantes, concretamente las [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center 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