{"id":"https://openalex.org/W7160647746","doi":"https://doi.org/10.48550/arxiv.2605.05276","title":"On Unbiased Parameter Estimation and Signal Reconstruction","display_name":"On Unbiased Parameter Estimation and Signal Reconstruction","publication_year":2026,"publication_date":"2026-05-06","ids":{"openalex":"https://openalex.org/W7160647746","doi":"https://doi.org/10.48550/arxiv.2605.05276"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2605.05276","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2605.05276","pdf_url":"https://arxiv.org/pdf/2605.05276","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2605.05276","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5065928639","display_name":"Joonas Lahtinen","orcid":"https://orcid.org/0000-0002-9377-8713","observed_orcids":["https://orcid.org/0000-0002-9377-8713"]},"institutions":[{"id":"https://openalex.org/I166825849","display_name":"Tampere University","ror":"https://ror.org/033003e23","country_code":"FI","type":"education","lineage":["https://openalex.org/I166825849"]},{"id":"https://openalex.org/I4387153886","display_name":"University of Tampere","ror":"https://ror.org/05p6qqk79","country_code":"FI","type":"education","lineage":["https://openalex.org/I166825849","https://openalex.org/I4387153886"]}],"countries":["FI"],"is_corresponding":true,"raw_author_name":"Lahtinen, Joonas","raw_affiliation_strings":["Mathematics & Computing Sciences , Tampere University , Tampere 33720 , Finland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Mathematics & Computing Sciences , Tampere University , Tampere 33720 , Finland","institution_ids":["https://openalex.org/I166825849","https://openalex.org/I4387153886"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5065928639"],"corresponding_institution_ids":["https://openalex.org/I166825849","https://openalex.org/I4387153886"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.8925999999046326,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.8925999999046326,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10931","display_name":"Direction-of-Arrival Estimation Techniques","score":0.06340000033378601,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11205","display_name":"Numerical methods in inverse problems","score":0.011599999852478504,"subfield":{"id":"https://openalex.org/subfields/2610","display_name":"Mathematical Physics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/unbiased-parameter-estimation","display_name":"unbiased parameter estimation","score":0.9210000038146973},{"id":"https://openalex.org/keywords/signal-reconstruction","display_name":"signal reconstruction","score":0.9169999957084656},{"id":"https://openalex.org/keywords/noise-robustness","display_name":"noise robustness","score":0.8569999933242798},{"id":"https://openalex.org/keywords/sensor-arrays","display_name":"sensor arrays","score":0.3370000123977661},{"id":"https://openalex.org/keywords/signal-to-noise-ratio","display_name":"signal-to-noise ratio","score":0.9179999828338623},{"id":"https://openalex.org/keywords/compressed-sensing","display_name":"compressed sensing","score":0.878000020980835}],"concepts":[{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.6258999705314636},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.600600004196167},{"id":"https://openalex.org/C2909318450","wikidata":"https://www.wikidata.org/wiki/Q15222032","display_name":"Unbiased Estimation","level":3,"score":0.5978000164031982},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5748999714851379},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.546500027179718},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.5403000116348267},{"id":"https://openalex.org/C70958404","wikidata":"https://www.wikidata.org/wiki/Q7512728","display_name":"Signal reconstruction","level":4,"score":0.4706999957561493},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.45829999446868896},{"id":"https://openalex.org/C4978587","wikidata":"https://www.wikidata.org/wiki/Q1138810","display_name":"Cram\u00e9r\u2013Rao bound","level":3,"score":0.3986999988555908},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.39590001106262207},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.3758000135421753},{"id":"https://openalex.org/C124851039","wikidata":"https://www.wikidata.org/wiki/Q2665459","display_name":"Compressed sensing","level":2,"score":0.367000013589859},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.3628999888896942},{"id":"https://openalex.org/C130648207","wikidata":"https://www.wikidata.org/wiki/Q6007562","display_name":"Imprecise probability","level":3,"score":0.36239999532699585},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.34790000319480896},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.34689998626708984},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.3467999994754791},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.32170000672340393},{"id":"https://openalex.org/C18294631","wikidata":"https://www.wikidata.org/wiki/Q2621185","display_name":"Location parameter","level":3,"score":0.3122999966144562},{"id":"https://openalex.org/C141379421","wikidata":"https://www.wikidata.org/wiki/Q6094427","display_name":"Iterative reconstruction","level":2,"score":0.31130000948905945},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.3111000061035156},{"id":"https://openalex.org/C122203268","wikidata":"https://www.wikidata.org/wiki/Q5862903","display_name":"Probability theory","level":2,"score":0.30300000309944153},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.29649999737739563},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2930000126361847},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.2865000069141388},{"id":"https://openalex.org/C67226441","wikidata":"https://www.wikidata.org/wiki/Q1665389","display_name":"Robust statistics","level":3,"score":0.27570000290870667},{"id":"https://openalex.org/C137270730","wikidata":"https://www.wikidata.org/wiki/Q120811","display_name":"Detection theory","level":3,"score":0.27559998631477356},{"id":"https://openalex.org/C196323059","wikidata":"https://www.wikidata.org/wiki/Q7862949","display_name":"U-statistic","level":4,"score":0.26429998874664307},{"id":"https://openalex.org/C13944312","wikidata":"https://www.wikidata.org/wiki/Q7512748","display_name":"Signal-to-noise ratio (imaging)","level":2,"score":0.2605000138282776},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.2565999925136566},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.250900000333786}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2605.05276","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2605.05276","pdf_url":"https://arxiv.org/pdf/2605.05276","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2605.05276","doi":"https://doi.org/10.48550/arxiv.2605.05276","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.05276","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2605.05276","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2605.05276","pdf_url":"https://arxiv.org/pdf/2605.05276","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321727","display_name":"Opetus- ja Kulttuuriministeri\u00f6","ror":"https://ror.org/02w52zt87"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7160647746.pdf","grobid_xml":"https://content.openalex.org/works/W7160647746.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7117371930","https://openalex.org/W1995463076","https://openalex.org/W2026930460","https://openalex.org/W335759638","https://openalex.org/W7096963513","https://openalex.org/W2104185459","https://openalex.org/W1806269746","https://openalex.org/W2952464103","https://openalex.org/W2593201075","https://openalex.org/W7096170705"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"extend":[4],"the":[5,51,57,67,85,89,104,109,115],"theory":[6],"of":[7,22,53,69,84,92,106],"depth-unbiased":[8],"source":[9],"localization":[10],"to":[11,65],"unbiased":[12,95],"parameter":[13],"estimation":[14,39],"and":[15,37,60,94,108],"signal":[16],"reconstruction":[17],"for":[18],"an":[19],"arbitrary":[20],"number":[21,52,105],"non-zero":[23,72],"parameters.":[24],"The":[25,44,78,97],"topic":[26],"touches":[27],"on":[28,50],"exact":[29],"reconstructibility,":[30],"most":[31],"commonly":[32],"studied":[33],"in":[34,56],"compressed":[35],"sensing":[36],"multisource":[38],"across":[40],"various":[41],"imaging":[42],"problems.":[43],"theoretical":[45,116],"results":[46],"derive":[47],"upper":[48],"bounds":[49],"recoverable":[54],"parameters":[55,73],"noiseless":[58],"case,":[59],"define":[61],"a":[62,81,101],"probability":[63],"measure":[64],"assess":[66],"likelihood":[68],"recovering":[70],"all":[71],"with":[74],"correct":[75],"magnitude":[76],"order.":[77],"work":[79],"provides":[80],"mathematical":[82],"explanation":[83],"open":[86],"question":[87],"regarding":[88],"noise":[90],"robustness":[91],"standardized":[93],"methods.":[96],"paper":[98],"also":[99],"reveals":[100],"trade-off":[102],"between":[103],"sensors":[107],"signal-to-noise":[110],"ratio.":[111],"Numerical":[112],"experiments":[113],"demonstrate":[114],"findings.":[117]},"counts_by_year":[],"updated_date":"2026-11-05T12:25:35.769452","created_date":"2026-05-09T00:00:00"}
