{"id":"https://openalex.org/W2159579069","doi":"https://doi.org/10.1198/004017004000000158","title":"Markov Chain Monte Carlo Estimation for the Two-Component Model","display_name":"Markov Chain Monte Carlo Estimation for the Two-Component Model","publication_year":2004,"publication_date":"2004-02-01","ids":{"openalex":"https://openalex.org/W2159579069","doi":"https://doi.org/10.1198/004017004000000158","mag":"2159579069"},"language":"en","primary_location":{"id":"doi:10.1198/004017004000000158","is_oa":false,"landing_page_url":"https://doi.org/10.1198/004017004000000158","pdf_url":null,"source":{"id":"https://openalex.org/S985303","display_name":"Technometrics","issn_l":"0040-1706","issn":["0040-1706","1537-2723"],"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":"Technometrics","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/A5023398799","display_name":"Geoffrey P. Jones","orcid":"https://orcid.org/0000-0002-6244-1245"},"institutions":[{"id":"https://openalex.org/I51158804","display_name":"Massey University","ror":"https://ror.org/052czxv31","country_code":"NZ","type":"education","lineage":["https://openalex.org/I51158804"]}],"countries":["NZ"],"is_corresponding":true,"raw_author_name":"Geoffrey Jones","raw_affiliation_strings":["Institute of Information Sciences and Technology, Massey University, Palmerston North, New Zealand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Information Sciences and Technology, Massey University, Palmerston North, New Zealand","institution_ids":["https://openalex.org/I51158804"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5023398799"],"corresponding_institution_ids":["https://openalex.org/I51158804"],"apc_list":null,"apc_paid":null,"fwci":0.9257,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.73852436,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"46","issue":"1","first_page":"99","last_page":"107"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9962999820709229,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9962999820709229,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11423","display_name":"Pesticide Residue Analysis and Safety","score":0.988099992275238,"subfield":{"id":"https://openalex.org/subfields/1106","display_name":"Food Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9794999957084656,"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/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.8591315150260925},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.6769916415214539},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.6350570917129517},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5609343647956848},{"id":"https://openalex.org/keywords/multiplicative-function","display_name":"Multiplicative function","score":0.5402886271476746},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4967358708381653},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.47632843255996704},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.4578153192996979},{"id":"https://openalex.org/keywords/hybrid-monte-carlo","display_name":"Hybrid Monte Carlo","score":0.4272440969944},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.39028701186180115},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.3626056909561157},{"id":"https://openalex.org/keywords/applied-mathematics","display_name":"Applied mathematics","score":0.3451617658138275},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.30764132738113403},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3042990565299988},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.1563231348991394},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.12332230806350708}],"concepts":[{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.8591315150260925},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.6769916415214539},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.6350570917129517},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5609343647956848},{"id":"https://openalex.org/C42747912","wikidata":"https://www.wikidata.org/wiki/Q1048447","display_name":"Multiplicative function","level":2,"score":0.5402886271476746},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4967358708381653},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.47632843255996704},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.4578153192996979},{"id":"https://openalex.org/C13153151","wikidata":"https://www.wikidata.org/wiki/Q1639846","display_name":"Hybrid Monte Carlo","level":4,"score":0.4272440969944},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.39028701186180115},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.3626056909561157},{"id":"https://openalex.org/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.3451617658138275},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.30764132738113403},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3042990565299988},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.1563231348991394},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.12332230806350708},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1198/004017004000000158","is_oa":false,"landing_page_url":"https://doi.org/10.1198/004017004000000158","pdf_url":null,"source":{"id":"https://openalex.org/S985303","display_name":"Technometrics","issn_l":"0040-1706","issn":["0040-1706","1537-2723"],"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":"Technometrics","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":19,"referenced_works":["https://openalex.org/W1982897420","https://openalex.org/W2029260608","https://openalex.org/W2036252721","https://openalex.org/W2040482262","https://openalex.org/W2045656233","https://openalex.org/W2060530519","https://openalex.org/W2078017724","https://openalex.org/W2093677679","https://openalex.org/W2130416410","https://openalex.org/W2130774920","https://openalex.org/W2136796925","https://openalex.org/W2146614932","https://openalex.org/W2556034601","https://openalex.org/W2767443199","https://openalex.org/W3004225063","https://openalex.org/W3204157529","https://openalex.org/W4232282966","https://openalex.org/W4237851683","https://openalex.org/W4240818092"],"related_works":["https://openalex.org/W4226314133","https://openalex.org/W2305965628","https://openalex.org/W3098348269","https://openalex.org/W3192708935","https://openalex.org/W3186706744","https://openalex.org/W4248057375","https://openalex.org/W2974901631","https://openalex.org/W227870709","https://openalex.org/W2959831473","https://openalex.org/W4323055853"],"abstract_inverted_index":{"The":[0],"two-component":[1],"model":[2,35,63],"for":[3,60],"measurement":[4],"errors,":[5],"which":[6],"incorporates":[7],"both":[8,61],"additive":[9],"and":[10,31,65,85,92],"multiplicative":[11],"disturbances":[12],"into":[13],"a":[14],"regression":[15],"model,":[16],"has":[17],"been":[18],"found":[19],"to":[20,57,72,78],"accurately":[21],"describe":[22],"the":[23,40,62,79],"observed":[24],"heteroscedascticity":[25],"in":[26,46,74],"analytical":[27],"chemistry":[28],"measurements.":[29],"Estimation":[30],"inference":[32],"using":[33],"this":[34,90],"are":[36,70],"difficult":[37],"because":[38],"of":[39,42,89],"presence":[41],"two":[43],"unobserved":[44],"errors":[45],"each":[47],"observation.":[48],"Markov":[49],"chain":[50],"Monte":[51],"Carlo":[52],"techniques":[53],"enable":[54],"exact":[55],"inferences":[56],"be":[58],"made":[59],"parameters":[64],"unknown":[66],"concentrations,":[67],"but":[68],"there":[69],"difficulties":[71],"overcome":[73],"achieving":[75],"fast":[76],"convergence":[77],"target":[80],"distribution.":[81],"This":[82],"article":[83],"describes":[84],"illustrates":[86],"an":[87],"implementation":[88],"method,":[91],"compares":[93],"it":[94],"with":[95],"other":[96],"estimation":[97],"methods.":[98]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2013,"cited_by_count":1}],"updated_date":"2026-06-14T06:11:07.267592","created_date":"2025-10-10T00:00:00"}