{"id":"https://openalex.org/W4387609908","doi":"https://doi.org/10.1080/03610918.2023.2265084","title":"Application of Kalman Filtering with Bayesian formulation in adaptive sampling","display_name":"Application of Kalman Filtering with Bayesian formulation in adaptive sampling","publication_year":2023,"publication_date":"2023-10-13","ids":{"openalex":"https://openalex.org/W4387609908","doi":"https://doi.org/10.1080/03610918.2023.2265084"},"language":"en","primary_location":{"id":"doi:10.1080/03610918.2023.2265084","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2023.2265084","pdf_url":null,"source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Communications in Statistics - Simulation and Computation","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5022748630","display_name":"Dipika Patra","orcid":"https://orcid.org/0000-0003-4318-1123"},"institutions":[{"id":"https://openalex.org/I26771391","display_name":"Jaipuria Institute of Management","ror":"https://ror.org/038dhfq97","country_code":"IN","type":"education","lineage":["https://openalex.org/I26771391"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Dipika Patra","raw_affiliation_strings":["Department of Statistics, Seth Anandram Jaipuria College","Department of Statistics, Seth Anandram Jaipuria College, Kolkata, West Bengal, India"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, Seth Anandram Jaipuria College","institution_ids":["https://openalex.org/I26771391"]},{"raw_affiliation_string":"Department of Statistics, Seth Anandram Jaipuria College, Kolkata, West Bengal, India","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080774838","display_name":"Sanghamitra Pal","orcid":"https://orcid.org/0000-0002-5752-8282"},"institutions":[{"id":"https://openalex.org/I55862774","display_name":"West Bengal State University","ror":"https://ror.org/04qs5en05","country_code":"IN","type":"education","lineage":["https://openalex.org/I55862774"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Sanghamitra Pal","raw_affiliation_strings":["Department of Statistics, West Bengal State University","Department of Statistics, West Bengal State University, Berunanpukuria, West Bengal, India"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, West Bengal State University","institution_ids":["https://openalex.org/I55862774"]},{"raw_affiliation_string":"Department of Statistics, West Bengal State University, Berunanpukuria, West Bengal, India","institution_ids":["https://openalex.org/I55862774"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5080774838"],"corresponding_institution_ids":["https://openalex.org/I55862774"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.14824041,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"54","issue":"3","first_page":"683","last_page":"695"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13030","display_name":"Survey Sampling and Estimation Techniques","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13030","display_name":"Survey Sampling and Estimation Techniques","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9927999973297119,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T13546","display_name":"Census and Population Estimation","score":0.9884999990463257,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"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/kalman-filter","display_name":"Kalman filter","score":0.6780216097831726},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6072483658790588},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5986302495002747},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5787288546562195},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5551641583442688},{"id":"https://openalex.org/keywords/adaptive-sampling","display_name":"Adaptive sampling","score":0.5258194208145142},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.4475182890892029},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.4426887333393097},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.43723559379577637},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35997825860977173},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.34019336104393005},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.292250394821167},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.21244704723358154},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11695358157157898},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.10122630000114441},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.08500716090202332},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.07692939043045044}],"concepts":[{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.6780216097831726},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6072483658790588},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5986302495002747},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5787288546562195},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5551641583442688},{"id":"https://openalex.org/C2781395549","wikidata":"https://www.wikidata.org/wiki/Q4680762","display_name":"Adaptive sampling","level":3,"score":0.5258194208145142},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.4475182890892029},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.4426887333393097},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.43723559379577637},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35997825860977173},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34019336104393005},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.292250394821167},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.21244704723358154},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11695358157157898},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.10122630000114441},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.08500716090202332},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.07692939043045044},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/03610918.2023.2265084","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2023.2265084","pdf_url":null,"source":{"id":"https://openalex.org/S153329750","display_name":"Communications in Statistics - Simulation and Computation","issn_l":"0361-0918","issn":["0361-0918","1532-4141"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Communications in Statistics - Simulation and Computation","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1913043124","https://openalex.org/W1957633800","https://openalex.org/W1971956681","https://openalex.org/W1979105999","https://openalex.org/W1983383161","https://openalex.org/W1986050895","https://openalex.org/W2020934227","https://openalex.org/W2023242222","https://openalex.org/W2029685080","https://openalex.org/W2047275340","https://openalex.org/W2081222242","https://openalex.org/W2091407124","https://openalex.org/W2091840866","https://openalex.org/W2092322680","https://openalex.org/W2105934661","https://openalex.org/W2114714448","https://openalex.org/W2147239019","https://openalex.org/W2233209916","https://openalex.org/W2288201536","https://openalex.org/W2323105696","https://openalex.org/W2330805404","https://openalex.org/W2514166826","https://openalex.org/W3000868504","https://openalex.org/W3097371405","https://openalex.org/W3121585020","https://openalex.org/W3132836078","https://openalex.org/W3195259359","https://openalex.org/W4214571706","https://openalex.org/W4230893583","https://openalex.org/W4231057675","https://openalex.org/W4233471163","https://openalex.org/W4285241589","https://openalex.org/W4388725587","https://openalex.org/W6617593460","https://openalex.org/W6696229275","https://openalex.org/W6723958736"],"related_works":["https://openalex.org/W2038693912","https://openalex.org/W1991602789","https://openalex.org/W1582396021","https://openalex.org/W4210985407","https://openalex.org/W312558119","https://openalex.org/W138014004","https://openalex.org/W2075598034","https://openalex.org/W1829869244","https://openalex.org/W2335441444","https://openalex.org/W4323041403"],"abstract_inverted_index":{"AbstractAn":[0],"extensive":[1],"amount":[2],"of":[3,19,37,40,72,78,110,159,214,221],"research":[4,82],"is":[5,25,49,103,120,147,170,175],"emphasized":[6],"on":[7],"survey":[8,95],"designs":[9],"and":[10,16,44,173],"estimation":[11,76,155,184],"procedures":[12],"related":[13],"to":[14,31,58,87,126,134,149,152,208],"rare":[15],"clustered":[17,45],"characteristics":[18],"a":[20,52,94,115,121],"population.":[21],"Adaptive":[22,79,97,160,186],"Sampling":[23,98,161],"design":[24],"the":[26,33,38,70,75,85,108,111,141,154,157,164,178,183,199,203,215,226],"most":[27],"applicable":[28],"probabilistic":[29],"technique":[30,139],"estimate":[32,109],"mean":[34],"or":[35],"total":[36],"variable":[39],"interest,":[41],"bearing":[42],"rarity":[43,48],"characteristics.":[46],"Since":[47],"regarded":[50],"as":[51],"time-dependent":[53],"feature,":[54],"such":[55],"surveys":[56],"need":[57,86],"be":[59],"organized":[60],"constantly":[61],"over":[62],"time.":[63],"No":[64],"studies":[65],"so":[66],"far":[67],"have":[68],"investigated":[69],"effect":[71],"time":[73],"in":[74,156],"context":[77,158],"Sampling.":[80],"This":[81,144],"therefore":[83],"captures":[84],"synthesize":[88],"this":[89,210],"periodic":[90],"information":[91],"when":[92],"conducting":[93],"using":[96],"design.":[99],"A":[100,167],"recursive":[101,124],"process":[102],"employed":[104],"here":[105,151],"that":[106,136,177],"improves":[107,182],"population":[112],"parameter":[113],"from":[114,202],"practical":[116],"perspective.":[117],"\u201cKalman":[118],"Filtering\u201d":[119],"well":[122],"known":[123],"procedure":[125],"use":[127,135],"past":[128,165],"data.":[129,166],"Later,":[130],"statisticians":[131],"were":[132],"able":[133],"Kalman":[137],"Filtering":[138],"with":[140],"Bayesian":[142,145],"formulation.":[143],"approach":[146,180],"proposed":[148],"employ":[150],"improve":[153],"design,":[162],"utilizing":[163],"simulation":[168],"study":[169],"carried":[171],"out":[172,213],"it":[174],"concluded":[176],"suggested":[179],"substantially":[181],"accuracy.Keywords:":[185],"SamplingGeneralized":[187],"regression":[188],"estimatorHorvitz\u2013Thompson":[189],"estimatorKalman":[190],"filteringSimulationMATHEMATICS":[191],"SUBJECT":[192],"CLASSIFICATION:":[193],"62D05":[194],"AcknowledgementsThe":[195],"authors":[196],"gratefully":[197],"acknowledge":[198],"support":[200],"received":[201],"referees":[204],"which":[205],"enabled":[206],"them":[207],"produce":[209],"improved":[211],"version":[212],"original":[216],"submission.Disclosure":[217],"statementNo":[218],"potential":[219],"conflict":[220],"interest":[222],"was":[223],"reported":[224],"by":[225],"author(s).":[227]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
