{"id":11204,"date":"2018-10-19T11:35:20","date_gmt":"2018-10-19T11:35:20","guid":{"rendered":"http:\/\/cio.edu.umh.es\/?p=11204"},"modified":"2018-10-19T11:35:20","modified_gmt":"2018-10-19T11:35:20","slug":"seminario-de-allan-seheult","status":"publish","type":"post","link":"https:\/\/cio.umh.es\/en\/2018\/10\/19\/seminario-de-allan-seheult\/","title":{"rendered":"Seminario de Allan Seheult"},"content":{"rendered":"<p>[:es][et_pb_section admin_label=\u00bbsection\u00bb][et_pb_row admin_label=\u00bbrow\u00bb][et_pb_column type=\u00bb4_4&#8243;][et_pb_text admin_label=\u00bbTexto\u00bb background_layout=\u00bblight\u00bb text_orientation=\u00bbleft\u00bb use_border_color=\u00bboff\u00bb border_color=\u00bb#ffffff\u00bb border_style=\u00bbsolid\u00bb]<br \/>\n<span style=\"color: #000000\"><strong>Speaker:<\/strong><\/span>\u00a0Allan Seheult (Durham University).<br \/>\n<strong><span style=\"color: #000000\">Title:<\/span>\u00a0<\/strong>\u201cBayesian Method of Moments (BMOM) Analysis of Mean and Regression Models\u201d.<br \/>\n<strong><span style=\"color: #000000\">Date:<\/span>\u00a0<\/strong>Lunes 29 de octubre, 11:00 horas.<br \/>\n<strong><span style=\"color: #000000\">Localication:<\/span>\u00a0<\/strong>Sala de seminarios\u00a0del CIO (Edificio Torretamarit).<br \/>\n<strong><span style=\"color: #000000\">Abstract.\u00a0<\/span><\/strong><span style=\"color: #000000\">A Bayesian method of moments\/instrumental variable (BMOM\/IV) approach is developed and applied in the analysis of the important mean and multiple regression models. Given a single set of data, it is shown how to obtain posterior and predictive moments without the use of likelihood functions, prior densities and Bayes\u2019 Theorem. The posterior and predictive moments, based on a few relatively weak assumptions, are then used to obtain maximum entropy densities for parameters, realized error terms and future values of variables. Posterior means for parameters and realized error terms are shown to be equal to certain well known estimates and rationalized in terms of quadratic loss functions. Conditional maxent posterior densities for means and regression coefficients given scale parameters are in the normal form while scale parameters\u2019 maxent densities are in the exponential form. Marginal densities for individual regression coefficients, realized error terms and future values are in the Laplace or double-exponential form with heavier tails than normal densities with the same means and variances. It is concluded that these results will be very useful, particularly when there is difficulty in formulating appropriate likelihood functions and prior densities needed in traditional maximum likelihood and Bayesian approaches.<\/span><br \/>\n[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][:]<\/p>","protected":false},"excerpt":{"rendered":"<p>[:es][et_pb_section admin_label=\u00bbsection\u00bb][et_pb_row admin_label=\u00bbrow\u00bb][et_pb_column type=\u00bb4_4&#8243;][et_pb_text admin_label=\u00bbTexto\u00bb background_layout=\u00bblight\u00bb text_orientation=\u00bbleft\u00bb use_border_color=\u00bboff\u00bb border_color=\u00bb#ffffff\u00bb border_style=\u00bbsolid\u00bb]<br \/>\nSpeaker:\u00a0Allan Seheult (Durham University).<br \/>\nTitle:\u00a0\u201cBayesian Method of Moments (BMOM) Analysis of Mean and Regression Models\u201d.<br \/>\nDate:\u00a0Lunes 29 de octubre, 11:00 horas.<br \/>\nLocalication:\u00a0Sala de seminarios\u00a0del CIO (Edificio Torretamarit).<br \/>\nAbstract.\u00a0A Bayesian method of moments\/instrumental variable (BMOM\/IV) approach is developed and applied in the analysis of the important mean and multiple regression models. [&#8230;]<\/p>","protected":false},"author":3477,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_links_to":"","_links_to_target":""},"categories":[4,873],"tags":[],"_links":{"self":[{"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/posts\/11204"}],"collection":[{"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/users\/3477"}],"replies":[{"embeddable":true,"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/comments?post=11204"}],"version-history":[{"count":0,"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/posts\/11204\/revisions"}],"wp:attachment":[{"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/media?parent=11204"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/categories?post=11204"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/cio.umh.es\/en\/wp-json\/wp\/v2\/tags?post=11204"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}