{"id":1879,"date":"2021-10-06T15:37:37","date_gmt":"2021-10-06T13:37:37","guid":{"rendered":"https:\/\/www.uni.lu\/fstm-fr\/events\/deconvolution-with-unknown-noise-distribution\/"},"modified":"2021-10-06T15:37:37","modified_gmt":"2021-10-06T13:37:37","slug":"deconvolution-with-unknown-noise-distribution","status":"publish","type":"events","link":"https:\/\/www.uni.lu\/fstm-fr\/events\/deconvolution-with-unknown-noise-distribution\/","title":{"rendered":"Deconvolution with unknown noise distribution"},"content":{"rendered":"<section class=\"wp-block-unilux-blocks-free-section section\"><div class=\"container xl:max-w-screen-xl\"><p><strong>Abstract<\/strong>: \u00ab\u00a0I consider the deconvolution problem in the case\u00a0where no information is known about the noise distribution. More precisely, no assumption is made on the noise distribution and no samples are available to estimate it: the deconvolution problem is solved based only on observations of the corrupted signal. I will prove the identifiability of the model up to translation when the signal has a Laplace transform with an exponential growth $rho$ smaller than 2 and when it can be decomposed into two dependent components, so that the identifiability theorem can be used for sequences of dependent data or for sequences of iid multidimensional data. \u00a0In the case of iid multidimensional \u00a0data, I will propose an adaptive estimator of the density of the signal and provide rates of convergence. This rate of convergence is known to be minimax when \u03c1 = 1. \u00ab\u00a0<\/p><\/div><\/section>","protected":false},"excerpt":{"rendered":"","protected":false},"author":0,"featured_media":1880,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"featured_image_focal_point":[],"show_featured_caption":false,"ulux_newsletter_groups":"","uluxPostTitle":"","uluxPrePostTitle":"","_trash_the_other_posts":false,"_price":"","_stock":"","_tribe_ticket_header":"","_tribe_default_ticket_provider":"","_tribe_ticket_capacity":"0","_ticket_start_date":"","_ticket_end_date":"","_tribe_ticket_show_description":"","_tribe_ticket_show_not_going":false,"_tribe_ticket_use_global_stock":"","_tribe_ticket_global_stock_level":"","_global_stock_mode":"","_global_stock_cap":"","_tribe_rsvp_for_event":"","_tribe_ticket_going_count":"","_tribe_ticket_not_going_count":"","_tribe_tickets_list":"[]","_tribe_ticket_has_attendee_info_fields":false,"event_start_date":"2021-10-14 16:00:00","event_end_date":"2021-10-14 17:00:00","event_speaker_name":"Pr. Elisabeth Gassiat ","event_speaker_link":"","event_is_online":false,"event_location":"Campus Belval\r\nMSA 3.530","event_street":"2, Avenue de l\u2019universit\u00e9","event_location_link":"","event_zip_code":"L-4365","event_city":"Esch-sur-Alzette","event_country":"LU"},"events-topic":[309],"events-type":[],"organisation":[60],"authorship":[],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v22.3 (Yoast SEO v22.3) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Deconvolution with unknown noise distribution - FSTM I Uni.lu<\/title>\n<meta name=\"description\" content=\"Abstract: &quot;I consider the deconvolution problem in the case\u00a0where no information is known about the noise distribution. 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More precisely, no assumption is\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.uni.lu\/fstm-fr\/events\/deconvolution-with-unknown-noise-distribution\/\" \/>\n<meta property=\"og:site_name\" content=\"FSTM FR\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/fstm.uni.lu\/\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/20\/2026\/03\/03111744\/FSTM_SM-Profile_1600x1600px-scaled.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"2560\" \/>\n\t<meta property=\"og:image:height\" content=\"2560\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Dur\u00e9e de lecture estim\u00e9e\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.uni.lu\/fstm-fr\/events\/deconvolution-with-unknown-noise-distribution\/\",\"url\":\"https:\/\/www.uni.lu\/fstm-fr\/events\/deconvolution-with-unknown-noise-distribution\/\",\"name\":\"Deconvolution with unknown noise distribution - FSTM I Uni.lu\",\"isPartOf\":{\"@id\":\"https:\/\/www.uni.lu\/fstm-fr\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/www.uni.lu\/fstm-fr\/events\/deconvolution-with-unknown-noise-distribution\/#primaryimage\"},\"image\":{\"@id\":\"https:\/\/www.uni.lu\/fstm-fr\/events\/deconvolution-with-unknown-noise-distribution\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/www.uni.lu\/wp-content\/uploads\/sites\/20\/2021\/10\/default-1.jpg\",\"datePublished\":\"2021-10-06T13:37:37+00:00\",\"dateModified\":\"2021-10-06T13:37:37+00:00\",\"description\":\"Abstract: \\\"I consider the deconvolution problem in the case\u00a0where no information is known about the noise distribution. 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