When: Friday, February 6, 3:00 PM
\nWhere: Tyler 055<\/p>\n
Abstract<\/strong> Bio<\/strong> When: Friday, February 6, 3:00 PM Where: Tyler 055 Abstract In this talk, we will first discuss the proposed \u03b1-separability for functional data. Functional data consist of random samples observed over a continuum, such as curves over a time range. These data often exhibit two kinds of variation: amplitude variation in the vertical direction and […]<\/p>\n","protected":false},"author":3300,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":"","_links_to":"","_links_to_target":""},"categories":[34,85],"tags":[],"class_list":["post-18168","post","type-post","status-publish","format-standard","hentry","category-news","category-seminars"],"acf":[],"_links":{"self":[{"href":"https:\/\/web.uri.edu\/cs\/wp-json\/wp\/v2\/posts\/18168","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/web.uri.edu\/cs\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/web.uri.edu\/cs\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/web.uri.edu\/cs\/wp-json\/wp\/v2\/users\/3300"}],"replies":[{"embeddable":true,"href":"https:\/\/web.uri.edu\/cs\/wp-json\/wp\/v2\/comments?post=18168"}],"version-history":[{"count":1,"href":"https:\/\/web.uri.edu\/cs\/wp-json\/wp\/v2\/posts\/18168\/revisions"}],"predecessor-version":[{"id":18169,"href":"https:\/\/web.uri.edu\/cs\/wp-json\/wp\/v2\/posts\/18168\/revisions\/18169"}],"wp:attachment":[{"href":"https:\/\/web.uri.edu\/cs\/wp-json\/wp\/v2\/media?parent=18168"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/web.uri.edu\/cs\/wp-json\/wp\/v2\/categories?post=18168"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/web.uri.edu\/cs\/wp-json\/wp\/v2\/tags?post=18168"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\nIn this talk, we will first discuss the proposed \u03b1-separability for functional data. Functional data consist of random samples observed over a continuum, such as curves over a time range. These data often exhibit two kinds of variation: amplitude variation in the vertical direction and phase variation in the horizontal direction. Separating them in an identifiable manner has been a long-standing challenge in functional data analysis. We introduce the notion of “\u03b1-separability” to rigorously address this issue, upon constructing a family of \u03b1-indexed metrics on the function space. We demonstrate how the metric-induced Fr\u00e9chet mean leads to the proposed \u03b1-separability and an adjustable model for functional data. The parameter \u03b1 allows user-defined modeling emphasis between vertical and horizontal features. We showcase our method in several real-data applications. If time permits, we will also discuss the proposed reweighted random forest for predicting health-related outcomes using human microbiome data and demonstrate its applications in the American Gut Project.<\/p>\n
\nDr. Tian Wang is a research scientist (official title: staff associate II) in the Department of Biostatistics at Columbia University. He also completed his postdoctoral training in the same department. He received a PhD in Mathematics and a master\u2019s degree in Statistics from Washington University in St. Louis. His current research interests include functional data, omics data, and interdisciplinary collaboration.<\/p>\n","protected":false},"excerpt":{"rendered":"