{"version":"1.0","provider_name":"IMDEA Networks","provider_url":"https:\/\/networks.imdea.org\/es","author_name":"Marta Dorado","author_url":"https:\/\/networks.imdea.org\/es\/author\/marta-doradoimdea-org\/","title":"Outlier Detection for Functional Data - IMDEA Networks","type":"rich","width":600,"height":338,"html":"<blockquote class=\"wp-embedded-content\" data-secret=\"1cyRN8NSwL\"><a href=\"https:\/\/networks.imdea.org\/es\/actualidad\/agenda-de-eventos\/outlier-detection-for-functional-data\/\">Outlier Detection for Functional Data<\/a><\/blockquote><iframe sandbox=\"allow-scripts\" security=\"restricted\" src=\"https:\/\/networks.imdea.org\/es\/actualidad\/agenda-de-eventos\/outlier-detection-for-functional-data\/embed\/#?secret=1cyRN8NSwL\" width=\"600\" height=\"338\" title=\"\u00abOutlier Detection for Functional Data\u00bb \u2014 IMDEA Networks\" data-secret=\"1cyRN8NSwL\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" class=\"wp-embedded-content\"><\/iframe><script type=\"text\/javascript\">\n\/* <![CDATA[ *\/\n\/*! This file is auto-generated *\/\n!function(d,l){\"use strict\";l.querySelector&&d.addEventListener&&\"undefined\"!=typeof URL&&(d.wp=d.wp||{},d.wp.receiveEmbedMessage||(d.wp.receiveEmbedMessage=function(e){var t=e.data;if((t||t.secret||t.message||t.value)&&!\/[^a-zA-Z0-9]\/.test(t.secret)){for(var s,r,n,a=l.querySelectorAll('iframe[data-secret=\"'+t.secret+'\"]'),o=l.querySelectorAll('blockquote[data-secret=\"'+t.secret+'\"]'),c=new RegExp(\"^https?:$\",\"i\"),i=0;i<o.length;i++)o[i].style.display=\"none\";for(i=0;i<a.length;i++)s=a[i],e.source===s.contentWindow&&(s.removeAttribute(\"style\"),\"height\"===t.message?(1e3<(r=parseInt(t.value,10))?r=1e3:~~r<200&&(r=200),s.height=r):\"link\"===t.message&&(r=new URL(s.getAttribute(\"src\")),n=new URL(t.value),c.test(n.protocol))&&n.host===r.host&&l.activeElement===s&&(d.top.location.href=t.value))}},d.addEventListener(\"message\",d.wp.receiveEmbedMessage,!1),l.addEventListener(\"DOMContentLoaded\",function(){for(var e,t,s=l.querySelectorAll(\"iframe.wp-embedded-content\"),r=0;r<s.length;r++)(t=(e=s[r]).getAttribute(\"data-secret\"))||(t=Math.random().toString(36).substring(2,12),e.src+=\"#?secret=\"+t,e.setAttribute(\"data-secret\",t)),e.contentWindow.postMessage({message:\"ready\",secret:t},\"*\")},!1)))}(window,document);\n\/\/# sourceURL=https:\/\/networks.imdea.org\/wp-includes\/js\/wp-embed.min.js\n\/* ]]> *\/\n<\/script>\n","thumbnail_url":"https:\/\/networks.imdea.org\/wp-content\/uploads\/2019\/12\/oluwasegun-ojo.jpg","thumbnail_width":450,"thumbnail_height":550,"description":"We consider the problem of outlier detection in the context of functional data analysis (FDA). Observations in FDA context are curves (or functions) so outlying functions can take different forms. Consequently, it is not only desirable to identify an outlying curve but also to understand why such curve is an outlier. To this end, we..."}