{"id":"https://openalex.org/W2587368596","doi":"https://doi.org/10.1080/03610918.2017.1291965","title":"Optimum mixture designs for the log-logistic dose\u2013response model with mixture of two similar compounds","display_name":"Optimum mixture designs for the log-logistic dose\u2013response model with mixture of two similar compounds","publication_year":2017,"publication_date":"2017-02-09","ids":{"openalex":"https://openalex.org/W2587368596","doi":"https://doi.org/10.1080/03610918.2017.1291965","mag":"2587368596"},"language":"en","primary_location":{"id":"doi:10.1080/03610918.2017.1291965","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2017.1291965","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/A5048154717","display_name":"Manisha Pal","orcid":"https://orcid.org/0000-0001-5790-0083"},"institutions":[{"id":"https://openalex.org/I106542073","display_name":"University of Calcutta","ror":"https://ror.org/01e7v7w47","country_code":"IN","type":"education","lineage":["https://openalex.org/I106542073"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Manisha Pal","raw_affiliation_strings":["Department of Statistics, University of Calcutta, Kolkata (Calcutta), West Bengal, India"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, University of Calcutta, Kolkata (Calcutta), West Bengal, India","institution_ids":["https://openalex.org/I106542073"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108920556","display_name":"Nripes Kumar Mandal","orcid":null},"institutions":[{"id":"https://openalex.org/I106542073","display_name":"University of Calcutta","ror":"https://ror.org/01e7v7w47","country_code":"IN","type":"education","lineage":["https://openalex.org/I106542073"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"N. K. Mandal","raw_affiliation_strings":["Department of Statistics, University of Calcutta, Kolkata (Calcutta), West Bengal, India"],"affiliations":[{"raw_affiliation_string":"Department of Statistics, University of Calcutta, Kolkata (Calcutta), West Bengal, India","institution_ids":["https://openalex.org/I106542073"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5108920556"],"corresponding_institution_ids":["https://openalex.org/I106542073"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.01582585,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"47","issue":"3","first_page":"800","last_page":"808"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11798","display_name":"Optimal Experimental Design Methods","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11798","display_name":"Optimal Experimental Design Methods","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11235","display_name":"Statistical Methods in Clinical Trials","score":0.9747999906539917,"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/T11423","display_name":"Pesticide Residue Analysis and Safety","score":0.9646999835968018,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.6578636765480042},{"id":"https://openalex.org/keywords/mixing","display_name":"Mixing (physics)","score":0.6544751524925232},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.6420746445655823},{"id":"https://openalex.org/keywords/potency","display_name":"Potency","score":0.5890257954597473},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5803831815719604},{"id":"https://openalex.org/keywords/binomial","display_name":"Binomial (polynomial)","score":0.5081305503845215},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4853464663028717},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.4723312258720398},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.46280378103256226},{"id":"https://openalex.org/keywords/bioassay","display_name":"Bioassay","score":0.4577535390853882},{"id":"https://openalex.org/keywords/value","display_name":"Value (mathematics)","score":0.4272753596305847},{"id":"https://openalex.org/keywords/toxicology","display_name":"Toxicology","score":0.3374706506729126},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.24286141991615295},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.21757283806800842},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.07636696100234985},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.06915009021759033}],"concepts":[{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.6578636765480042},{"id":"https://openalex.org/C138777275","wikidata":"https://www.wikidata.org/wiki/Q6884054","display_name":"Mixing (physics)","level":2,"score":0.6544751524925232},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.6420746445655823},{"id":"https://openalex.org/C57992300","wikidata":"https://www.wikidata.org/wiki/Q2066956","display_name":"Potency","level":3,"score":0.5890257954597473},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5803831815719604},{"id":"https://openalex.org/C2781315470","wikidata":"https://www.wikidata.org/wiki/Q193623","display_name":"Binomial (polynomial)","level":2,"score":0.5081305503845215},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4853464663028717},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.4723312258720398},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.46280378103256226},{"id":"https://openalex.org/C104488531","wikidata":"https://www.wikidata.org/wiki/Q864212","display_name":"Bioassay","level":2,"score":0.4577535390853882},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.4272753596305847},{"id":"https://openalex.org/C33070731","wikidata":"https://www.wikidata.org/wiki/Q7218","display_name":"Toxicology","level":1,"score":0.3374706506729126},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.24286141991615295},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.21757283806800842},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.07636696100234985},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.06915009021759033},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C202751555","wikidata":"https://www.wikidata.org/wiki/Q221681","display_name":"In vitro","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/03610918.2017.1291965","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03610918.2017.1291965","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":19,"referenced_works":["https://openalex.org/W1518054849","https://openalex.org/W2002374079","https://openalex.org/W2005960624","https://openalex.org/W2008661042","https://openalex.org/W2017327170","https://openalex.org/W2019589077","https://openalex.org/W2036065701","https://openalex.org/W2053500106","https://openalex.org/W2053792242","https://openalex.org/W2064868327","https://openalex.org/W2066180710","https://openalex.org/W2070779353","https://openalex.org/W2075562155","https://openalex.org/W2080376342","https://openalex.org/W2132855180","https://openalex.org/W2142548738","https://openalex.org/W2244947076","https://openalex.org/W2904339734","https://openalex.org/W2965081642"],"related_works":["https://openalex.org/W2073076114","https://openalex.org/W2368899332","https://openalex.org/W2117132430","https://openalex.org/W2188407313","https://openalex.org/W2186887461","https://openalex.org/W2040231334","https://openalex.org/W1709688772","https://openalex.org/W2022638109","https://openalex.org/W2402311171","https://openalex.org/W2183453817"],"abstract_inverted_index":{"The":[0,14,42],"article":[1],"studies":[2],"the":[3,11,19,37,47,54,57,64,73,82,87,92,101,104],"log-logistic":[4],"class":[5],"of":[6,24,34,56,60,66,72,94,103],"dose\u2013response":[7],"bioassay":[8],"models":[9],"in":[10,100],"binomial":[12],"set-up.":[13],"dose":[15],"is":[16,44],"identified":[17],"by":[18],"potency":[20,67],"adjusted":[21],"mixing":[22,88],"proportions":[23,89],"two":[25],"similar":[26],"compounds.":[27],"Models":[28],"for":[29,53,63,68],"both":[30],"absence":[31,102],"and":[32,49],"presence":[33],"interaction":[35,105],"between":[36],"compounds":[38],"have":[39],"been":[40],"considered.":[41],"aim":[43],"to":[45,80,85],"investigate":[46],"D-":[48],"Ds-optimal":[50],"mixture":[51],"designs":[52],"estimation":[55,65],"full":[58],"set":[59],"parameters":[61],"or":[62],"a":[69,97],"best":[70],"guess":[71],"parameter":[74],"values.":[75],"We":[76],"also":[77],"indicate":[78],"how":[79],"find":[81],"optimal":[83],"design":[84],"estimate":[86],"at":[90],"which":[91],"probability":[93],"success":[95],"attains":[96],"given":[98],"value":[99],"effect.":[106]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
