{"id":"https://openalex.org/W7135073728","doi":"https://doi.org/10.1177/15741699261424802","title":"Log-Ratio Estimation in Survey Sampling: A Comparative Study Using Agricultural and Cancer Data with Simulations","display_name":"Log-Ratio Estimation in Survey Sampling: A Comparative Study Using Agricultural and Cancer Data with Simulations","publication_year":2026,"publication_date":"2026-03-04","ids":{"openalex":"https://openalex.org/W7135073728","doi":"https://doi.org/10.1177/15741699261424802"},"language":"en","primary_location":{"id":"doi:10.1177/15741699261424802","doi":"https://doi.org/10.1177/15741699261424802","is_oa":false,"landing_page_url":"https://doi.org/10.1177/15741699261424802","pdf_url":null,"source":{"id":"https://openalex.org/S2765066696","display_name":"Model Assisted Statistics and Applications","issn_l":"1574-1699","issn":["1574-1699","1875-9068"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["abdc-c","cwts-core","jufo-1"],"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Model Assisted Statistics and Applications","raw_type":"journal-article"},"volume":null,"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/A5128885404","display_name":"N. Venkata Lakshmi","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4401726783","display_name":"VIT-AP University","ror":"https://ror.org/007v4hf75","country_code":null,"type":"education","lineage":["https://openalex.org/I4401726783"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"N. Venkata Lakshmi","raw_affiliation_strings":["Department of Mathematics, School of Advanced Sciences, VIT-AP University, Amaravati, Andhra Pradesh, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mathematics, School of Advanced Sciences, VIT-AP University, Amaravati, Andhra Pradesh, India","institution_ids":["https://openalex.org/I4401726783"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075848744","display_name":"Faizan Danish","orcid":"https://orcid.org/0000-0002-0476-9744","observed_orcids":["https://orcid.org/0000-0002-0476-9744"]},"institutions":[{"id":"https://openalex.org/I4401726783","display_name":"VIT-AP University","ror":"https://ror.org/007v4hf75","country_code":null,"type":"education","lineage":["https://openalex.org/I4401726783"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Faizan Danish","raw_affiliation_strings":["Department of Mathematics, School of Advanced Sciences, VIT-AP University, Amaravati, Andhra Pradesh, India"],"raw_orcid":"https://orcid.org/0000-0002-0476-9744","affiliations":[{"raw_affiliation_string":"Department of Mathematics, School of Advanced Sciences, VIT-AP University, Amaravati, Andhra Pradesh, India","institution_ids":["https://openalex.org/I4401726783"]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5075848744"],"corresponding_institution_ids":["https://openalex.org/I4401726783"],"apc_list":{"value":4150,"currency":"USD","value_usd":4150},"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.21384285,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"21","issue":"2","first_page":"107","last_page":"117"},"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/T13546","display_name":"Census and Population Estimation","score":0.0,"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/T11798","display_name":"Optimal Experimental Design Methods","score":0.0,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/finite-population-mean-estimation","display_name":"finite population mean estimation","score":0.8989999890327454},{"id":"https://openalex.org/keywords/auxiliary-information","display_name":"auxiliary information","score":0.9319999814033508},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"mean squared error","score":0.7089999914169312},{"id":"https://openalex.org/keywords/relative-efficiency","display_name":"relative efficiency","score":0.953000009059906},{"id":"https://openalex.org/keywords/agricultural-data","display_name":"agricultural data","score":0.4169999957084656},{"id":"https://openalex.org/keywords/cancer-mortality","display_name":"cancer mortality","score":0.47999998927116394},{"id":"https://openalex.org/keywords/comparison-of-estimators","display_name":"estimator comparison","score":0.5410000085830688}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.8790000081062317},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.6183000206947327},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.5400000214576721},{"id":"https://openalex.org/C5733905","wikidata":"https://www.wikidata.org/wiki/Q10744315","display_name":"Survey sampling","level":3,"score":0.5145999789237976},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.5145000219345093},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.5037000179290771},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.49050000309944153},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.46799999475479126},{"id":"https://openalex.org/C129963666","wikidata":"https://www.wikidata.org/wiki/Q17105857","display_name":"Small area estimation","level":3,"score":0.46639999747276306},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4406999945640564},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.436599999666214},{"id":"https://openalex.org/C17648541","wikidata":"https://www.wikidata.org/wiki/Q2265984","display_name":"Efficiency","level":3,"score":0.37369999289512634},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.37130001187324524},{"id":"https://openalex.org/C39927690","wikidata":"https://www.wikidata.org/wiki/Q11197","display_name":"Logarithm","level":2,"score":0.35280001163482666},{"id":"https://openalex.org/C118518473","wikidata":"https://www.wikidata.org/wiki/Q11451","display_name":"Agriculture","level":2,"score":0.3483999967575073},{"id":"https://openalex.org/C191393472","wikidata":"https://www.wikidata.org/wiki/Q15222032","display_name":"Bias of an estimator","level":4,"score":0.32670000195503235},{"id":"https://openalex.org/C153701036","wikidata":"https://www.wikidata.org/wiki/Q659974","display_name":"Trustworthiness","level":2,"score":0.32330000400543213},{"id":"https://openalex.org/C2779585090","wikidata":"https://www.wikidata.org/wiki/Q3457762","display_name":"Resilience (materials science)","level":2,"score":0.3215000033378601},{"id":"https://openalex.org/C18747219","wikidata":"https://www.wikidata.org/wiki/Q620994","display_name":"Standard error","level":2,"score":0.30640000104904175},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.29269999265670776},{"id":"https://openalex.org/C102592046","wikidata":"https://www.wikidata.org/wiki/Q7504144","display_name":"Shrinkage estimator","level":5,"score":0.2896000146865845},{"id":"https://openalex.org/C89337504","wikidata":"https://www.wikidata.org/wiki/Q4828276","display_name":"Average treatment effect","level":3,"score":0.28439998626708984},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.2669999897480011},{"id":"https://openalex.org/C35594927","wikidata":"https://www.wikidata.org/wiki/Q2265984","display_name":"Efficient estimator","level":4,"score":0.2587999999523163},{"id":"https://openalex.org/C198477413","wikidata":"https://www.wikidata.org/wiki/Q7647069","display_name":"Survey data collection","level":2,"score":0.25130000710487366}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1177/15741699261424802","doi":"https://doi.org/10.1177/15741699261424802","is_oa":false,"landing_page_url":"https://doi.org/10.1177/15741699261424802","pdf_url":null,"source":{"id":"https://openalex.org/S2765066696","display_name":"Model Assisted Statistics and Applications","issn_l":"1574-1699","issn":["1574-1699","1875-9068"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["abdc-c","cwts-core","jufo-1"],"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Model Assisted Statistics and Applications","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"sustainable_development_goals_aurora":[{"id":"https://metadata.un.org/sdg/2","display_name":"Zero hunger","score":0.5412872433662415}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W46491787","https://openalex.org/W1510253928","https://openalex.org/W1987498850","https://openalex.org/W2052448331","https://openalex.org/W2128503603","https://openalex.org/W2132531707","https://openalex.org/W2162660442","https://openalex.org/W2165235213","https://openalex.org/W2483675240","https://openalex.org/W2965444367","https://openalex.org/W3168643724","https://openalex.org/W4292712310","https://openalex.org/W4392476951","https://openalex.org/W4392528652","https://openalex.org/W4393374846","https://openalex.org/W4401080173","https://openalex.org/W4406458323","https://openalex.org/W4408089201"],"related_works":[],"abstract_inverted_index":{"The":[0,89,192],"estimation":[1],"of":[2,23,32,54,83,120,171,177,258],"finite":[3],"population":[4,169],"characteristics,":[5],"particularly":[6,234],"the":[7,21,28,38,84,125,187,200,225,261],"mean,":[8],"is":[9,93,244],"critical":[10,60],"in":[11,76],"survey":[12,250],"sampling,":[13,251],"as":[14,123],"it":[15],"has":[16],"a":[17,68,77,118,164,168,174,245],"direct":[18],"impact":[19],"on":[20,130,138],"validity":[22],"sample":[24,175],"data":[25,34],"results.":[26],"As":[27,240],"number":[29],"and":[30,43,86,99,110,141,153,155,173,190,194,206,216,231],"complexity":[31],"available":[33],"increases,":[35],"so":[36],"does":[37],"demand":[39],"for":[40,61,248],"more":[41,229],"robust":[42],"precise":[44],"estimators.":[45,220],"Traditional":[46],"estimators":[47,101],"frequently":[48],"fail":[49],"to":[50,182,185,253],"take":[51],"full":[52],"advantage":[53],"auxiliary":[55,74,87],"information,":[56],"which":[57],"can":[58],"be":[59],"enhancing":[62],"estimating":[63,124,133,255],"accuracy.":[64],"This":[65],"paper":[66],"presents":[67],"novel":[69],"log-ratio":[70,90,201,226],"estimator":[71,202,227,243],"that":[72,199,224],"incorporates":[73],"information":[75],"non-linear":[78],"manner":[79],"using":[80,102],"logarithmic":[81],"transformations":[82],"study":[85,166],"variables.":[88],"estimator's":[91],"performance":[92],"assessed":[94],"against":[95],"9":[96],"different":[97],"classical":[98],"modern":[100],"two":[103],"essential":[104],"metrics:":[105],"mean":[106],"squared":[107],"error":[108],"(MSE)":[109],"percentage":[111],"relative":[112],"efficiency.":[113],"Our":[114],"empirical":[115,193],"analysis":[116],"includes":[117],"variety":[119],"applications,":[121,163],"such":[122],"area":[126],"under":[127],"wheat":[128],"based":[129,137],"cultivated":[131],"area,":[132,140],"peppermint":[134],"oil":[135],"production":[136],"field":[139],"three":[142],"real-world":[143],"datasets:":[144],"breast":[145],"cancer":[146,148],"deaths,":[147],"deaths":[149],"by":[150,264],"gender":[151],"(male":[152],"female),":[154],"brain":[156],"tumor":[157],"survival":[158],"rates.":[159],"To":[160],"supplement":[161],"these":[162],"simulation":[165,195],"with":[167,237,260],"size":[170,176],"100,000":[172,183],"1,000":[178],"was":[179],"run":[180],"up":[181],"times":[184],"assess":[186],"estimators\u2019":[188],"resilience":[189],"stability.":[191],"results":[196],"consistently":[197],"reveal":[198],"produces":[203],"lower":[204],"MSE":[205],"higher":[207],"PRE":[208],"values":[209],"across":[210],"all":[211],"datasets,":[212],"exhibiting":[213],"greater":[214],"accuracy":[215],"efficiency":[217],"over":[218],"traditional":[219],"These":[221],"findings":[222],"suggest":[223],"gives":[228],"trustworthy":[230],"efficient":[232],"estimates,":[233],"when":[235],"dealing":[236,259],"complicated":[238],"data.":[239],"such,":[241],"this":[242],"promising":[246],"tool":[247],"future":[249],"helping":[252],"enhance":[254],"methodologies":[256],"capable":[257],"challenges":[262],"provided":[263],"modern,":[265],"large-scale":[266],"datasets.":[267]},"counts_by_year":[],"updated_date":"2026-08-21T09:56:20.448147","created_date":"2026-03-13T00:00:00"}
