{"id":16245,"date":"2018-03-09T09:03:46","date_gmt":"2018-03-09T09:03:46","guid":{"rendered":"https:\/\/www.deberes.net\/tesis\/sin-categoria\/diagnostico-bayesianos-de-modelos\/"},"modified":"2018-03-09T09:03:46","modified_gmt":"2018-03-09T09:03:46","slug":"diagnostico-bayesianos-de-modelos","status":"publish","type":"post","link":"https:\/\/www.deberes.net\/tesis\/matematicas\/diagnostico-bayesianos-de-modelos\/","title":{"rendered":"Diagn\u00f3stico bayesianos de modelos"},"content":{"rendered":"

Tesis doctoral de Castellanos Nueda M. Eugenia <\/strong><\/h2>\n

La memoria trata del diagn\u00f3stico de modelos. Entendemos por tal la investigaci\u00f3n de la compatibilidad de los datos observados con el modelo supuesto sin considerar expl\u00edcitamente modelos alternativos. En el caso de proponer modelos alternativos hablar\u00edamos de \u00abselecci\u00f3n de modelos\u00bb. En la mayor\u00eda investigamos distintos m\u00e9todos de diagn\u00f3stico de modelos desde una perspectiva baysiana, compar\u00e1ndolos tambi\u00e9n con alg\u00fan m\u00e9todo est\u00e1ndar de la estad\u00edstica frecuentista. supongamos el modelo param\u00e9trico ho: x – f(x\/mu) que modeliza la muestra x observada en la poblaci\u00f3n de inter\u00e9s. El problema de diagnosticar ho puede ser planteado como el resultado de distintas elecciones de las siguientes herramientas: 1,- un estad\u00edstico de diagn\u00f3stico t = t(x) que mide la incompatibilidad de los datos observados, t(xobs) = tobs, con el modelo. 2,- una distribuci\u00f3n para t completamente especificada, es decir que no depende de par\u00e1metros desconocidos, bajo ho. 3,- una medida que localice el valor observado tobs en la distribuci\u00f3n para t, de manera que se cuantifique lo compatible o incompatible que son los datos con el modelo asumido. Las denominamos \u00abmedidas de sorpresa\u00bb. la correcta elecci\u00f3n de cada elemento es importante y conlleva que el m\u00e9todo de diagn\u00f3stico sea m\u00e1s o menos eficaz. Nosotros nos centramos en investigar distintas elecciones de (2) y (3), siendo el objetivo primordial estudiar cu\u00e1l es la elecci\u00f3n \u00f3ptima de (2), para cualquier elecci\u00f3n que se haga de t y de la medida de sorpresa. a lo largo de toda la memoria utilizamos dos medidas de sorpresa, el p-valor y la sorpresa predictiva relativa: para t >>h(t) bajo ho, y si valores grandes de t indican sorpresa, ambas medidas se definen como: ph(t) = pr ho (t>tobs) spr-ho = h(tobs)\/sup-t h(t) cuando la distribuci\u00f3n h(t) est\u00e1 completamente especificada, ambas medidas est\u00e1n perfectamente definidas. Ahora bien, en el caso en que dependa de<\/p>\n

 <\/p>\n

Datos acad\u00e9micos de la tesis doctoral \u00abDiagn\u00f3stico bayesianos de modelos<\/strong>\u00ab<\/h3>\n
    \n
  • T\u00edtulo de la tesis:<\/strong>\u00a0 Diagn\u00f3stico bayesianos de modelos <\/li>\n
  • Autor:<\/strong>\u00a0 Castellanos Nueda M. Eugenia <\/li>\n
  • Universidad:<\/strong>\u00a0 Miguel hern\u00e1ndez de elche<\/li>\n
  • Fecha de lectura de la tesis:<\/strong>\u00a0 22\/03\/2002<\/li>\n<\/ul>\n

     <\/p>\n

    Direcci\u00f3n y tribunal<\/h3>\n
      \n
    • Director de la tesis<\/strong>\n
        \n
      • Bayarri Garc\u00eda M. Jes\u00fas<\/li>\n<\/ul>\n<\/li>\n
      • Tribunal<\/strong>\n
          \n
        • Presidente del tribunal: domingo Morales gonzalez <\/li>\n
        • Juan Ferr\u00e1ndiz ferragud (vocal)<\/li>\n
        • el\u00edas Moreno bas (vocal)<\/li>\n
        • walter Racugno (vocal)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n

           <\/p>\n","protected":false},"excerpt":{"rendered":"

          Tesis doctoral de Castellanos Nueda M. Eugenia La memoria trata del diagn\u00f3stico de modelos. Entendemos por tal la investigaci\u00f3n de […]<\/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 center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}},"footnotes":""},"categories":[16550,1477,2764,126,37169,5688,4225],"tags":[8073,51429,5690,4228,40410,51430],"class_list":["post-16245","post","type-post","status-publish","format-standard","hentry","category-computacion-en-estadistica","category-estadistica","category-fundamentos-de-la-inferencia-estadistica","category-matematicas","category-miguel-hernandez-de-elche","category-tecnicas-de-inferencia-estadistica","category-teoria-de-la-distribucion-y-probabilidad","tag-bayarri-garcia-m-jesus","tag-castellanos-nueda-m-eugenia","tag-domingo-morales-gonzalez","tag-elias-moreno-bas","tag-juan-ferrandiz-ferragud","tag-walter-racugno"],"_links":{"self":[{"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/posts\/16245","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/comments?post=16245"}],"version-history":[{"count":0,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/posts\/16245\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/media?parent=16245"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/categories?post=16245"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/tags?post=16245"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}