{"id":"https://openalex.org/W2517139653","doi":"https://doi.org/10.1080/03610918.2015.1056354","title":"A non-iterative posterior sampling algorithm for Laplace linear regression model","display_name":"A non-iterative posterior sampling algorithm for Laplace linear regression model","publication_year":2015,"publication_date":"2015-06-30","ids":{"openalex":"https://openalex.org/W2517139653","doi":"https://doi.org/10.1080/03610918.2015.1056354","mag":"2517139653"},"language":"en","primary_location":{"id":"doi:10.1080/03610918.2015.1056354","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2015.1056354","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/A5083368093","display_name":"Fengkai Yang","orcid":"https://orcid.org/0000-0003-4262-1043"},"institutions":[{"id":"https://openalex.org/I154099455","display_name":"Shandong University","ror":"https://ror.org/0207yh398","country_code":"CN","type":"education","lineage":["https://openalex.org/I154099455"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fengkai Yang","raw_affiliation_strings":["School of Mathematics, Shandong University, Jinan, China;School of Mathematics and Statistics, Shandong University, Weihai, China","School of Mathematics, Shandong University, Jinan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematics, Shandong University, Jinan, China;School of Mathematics and Statistics, Shandong University, Weihai, China","institution_ids":["https://openalex.org/I154099455"]},{"raw_affiliation_string":"School of Mathematics, Shandong University, Jinan, China","institution_ids":["https://openalex.org/I154099455"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050521031","display_name":"Haijing Yuan","orcid":null},"institutions":[{"id":"https://openalex.org/I154099455","display_name":"Shandong University","ror":"https://ror.org/0207yh398","country_code":"CN","type":"education","lineage":["https://openalex.org/I154099455"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Haijing Yuan","raw_affiliation_strings":["School of Mathematics, Shandong University, Jinan, China;School of Mathematics and Statistics, Shandong University, Weihai, China","School of Mathematics, Shandong University, Jinan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematics, Shandong University, Jinan, China;School of Mathematics and Statistics, Shandong University, Weihai, China","institution_ids":["https://openalex.org/I154099455"]},{"raw_affiliation_string":"School of Mathematics, Shandong University, Jinan, China","institution_ids":["https://openalex.org/I154099455"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5050521031"],"corresponding_institution_ids":["https://openalex.org/I154099455"],"apc_list":null,"apc_paid":null,"fwci":0.4365,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.79714819,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"46","issue":"3","first_page":"2488","last_page":"2503"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9983000159263611,"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"}},"topics":[{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9983000159263611,"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/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.9968000054359436,"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9961000084877014,"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/gibbs-sampling","display_name":"Gibbs sampling","score":0.5701602697372437},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.49651461839675903},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.46728524565696716},{"id":"https://openalex.org/keywords/posterior-probability","display_name":"Posterior probability","score":0.4528525471687317},{"id":"https://openalex.org/keywords/resampling","display_name":"Resampling","score":0.44089990854263306},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4352363646030426},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.433460533618927},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.4287453293800354},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.42586612701416016},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3064125180244446},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.24897116422653198}],"concepts":[{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.5701602697372437},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.49651461839675903},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.46728524565696716},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.4528525471687317},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.44089990854263306},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4352363646030426},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.433460533618927},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.4287453293800354},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.42586612701416016},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3064125180244446},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.24897116422653198},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/03610918.2015.1056354","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2015.1056354","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":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W45374770","https://openalex.org/W568726942","https://openalex.org/W1510097446","https://openalex.org/W1529876150","https://openalex.org/W1963745403","https://openalex.org/W1978901787","https://openalex.org/W1980503918","https://openalex.org/W1982652137","https://openalex.org/W1998073888","https://openalex.org/W1998487261","https://openalex.org/W2031817855","https://openalex.org/W2059416780","https://openalex.org/W2066323739","https://openalex.org/W2068722138","https://openalex.org/W2073259699","https://openalex.org/W2131012367","https://openalex.org/W2290731405","https://openalex.org/W3144545015"],"related_works":["https://openalex.org/W2123736748","https://openalex.org/W2094015288","https://openalex.org/W4231537836","https://openalex.org/W1243940979","https://openalex.org/W2335394797","https://openalex.org/W3116283813","https://openalex.org/W2330406685","https://openalex.org/W30749234","https://openalex.org/W2408349094","https://openalex.org/W3201306392"],"abstract_inverted_index":{"ABSTRACTIn":[0],"this":[1,129],"article,":[2],"a":[3],"non-iterative":[4],"sampling":[5,52],"algorithm":[6,42],"is":[7,120,132,148],"developed":[8],"to":[9,68,82],"obtain":[10],"an":[11],"independently":[12],"and":[13,37,53,72,107,113],"identically":[14],"distributed":[15],"samples":[16,55],"approximately":[17],"from":[18,57],"the":[19,31,41,44,49,54,70,75,84,87,105,126],"posterior":[20],"distribution":[21],"of":[22,46,74,86,123,128,139,143,151],"parameters":[23],"in":[24,48],"Laplace":[25],"linear":[26,95],"regression":[27],"model.":[28],"By":[29],"combining":[30],"inverse":[32],"Bayes":[33,93],"formulae,":[34],"sampling/importance":[35],"resampling,":[36],"expectation":[38],"maximum":[39],"algorithm,":[40],"eliminates":[43],"diagnosis":[45],"convergence":[47],"iterative":[50],"Gibbs":[51],"generated":[56],"it":[58],"can":[59],"be":[60],"used":[61],"for":[62,109],"inferences":[63],"immediately.":[64],"Simulations":[65],"are":[66,80],"conducted":[67],"illustrate":[69],"robustness":[71],"effectiveness":[73],"algorithm.":[76],"Finally,":[77],"real":[78],"data":[79],"studied":[81],"show":[83],"usefulness":[85],"proposed":[88],"methodology.KEYWORDS:":[89],"EM":[90],"algorithmGibbs":[91],"samplingInverse":[92],"formulaeLaplace":[94],"regressionSampling/important":[96],"resamplingMATHEMATICS":[97],"SUBJECT":[98],"CLASSIFICATION:":[99],"62F1562J0562D9965C60":[100],"AcknowledgmentsThe":[101],"authors":[102,116,145],"gratefully":[103],"acknowledge":[104],"editor":[106],"referees":[108],"their":[110],"valuable":[111],"comments":[112],"suggestions.":[114],"The":[115,135],"declare":[117,146],"that":[118],"there":[119,147],"no":[121,149],"conflict":[122,150],"interests":[124],"regarding":[125],"publication":[127],"article.FundingThis":[130],"research":[131],"supported":[133],"by":[134],"National":[136],"Science":[137],"Foundation":[138],"China":[140],"Grants":[141],"11371227.Conflict":[142],"interestThe":[144],"interest.":[152]},"counts_by_year":[{"year":2016,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
