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1、NORTHSTAR:AParameterEstimationMethodfortheSpatialAutoregressionModelTechnicalReportDepartmentofComputerScienceandEngineeringUniversityofMinnesota4-192EECSBuilding200UnionStreetSEMinneapolis,MN55455-0159USATR07-004NORTHSTAR:AParameterEstimationMethodfortheSpatialAutoregression
2、ModelMeteCelik,BarisM.Kazar,ShashiShekhar,DanielBoley,andDavidJ.LiljaFebruary09,20071NORTHSTAR:AParameterEstimationMethodfortheSpatialAutoregressionModelMeteCelik,BarisM.Kazar,ShashiShekhar,DanielBoley,DavidJ.LiljaAbstractParameterestimationmethodforthespatialautoregressionmo
3、del(SAR)isimportantbecauseofthemanyapplicationdomains,suchasregionaleconomics,ecology,environmentalmanagement,publicsafety,transportation,publichealth,business,travelandtourism.However,itiscomputationallyveryexpensivebecauseoftheneedtocomputethedeterminantofalargematrixduetoM
4、aximumLikelihoodTheory.Thelimitationofpreviousstudiesistheneedfornumerouscomputationsofthecomputationallyexpensivedeterminanttermofthelikelihoodfunction.Inthispaper,wepresentafaster,scalableandNOvelpRedictionandestimationTecHniquefortheexactSpaTialAutoRegressionmodelsolution(
5、NORTHSTAR).Weprovideaproofofthecorrectnessofthisalgorithmbyshowingtheobjectivefunctiontobeunimodular.AnalyticalandexperimentalresultsshowthattheNORTHSTARalgorithmiscomputationallyfasterthantherelatedapproaches,becauseitreducesthenumberofevaluationsofthedeterminantterminthelik
6、elihoodfunction.IndexTermsSpatialAutoregressionModel,SpatialAutocorrelation,SpatialDataMining,SpatialDatabases,MaximumLikelihoodTheory.ThisworkwaspartiallysupportedbytheArmyHighPerformanceComputingResearchCenter(AHPCRC)undertheauspicesoftheDepartmentoftheArmy,ArmyResearchLabo
7、ratory(ARL)undercontractnumberDAAD19-01-2-0014,andtheNSFgrantIIS-0534286.ThisworkreceivedadditionalsupportfromtheUniversityofMinnesotaDigitalTechnologyCenterandtheMinnesotaSupercomputingInstitute.M.CelikiswiththeCSDepartment,UniversityofMinnesota,Minneapolis,MN,55455.E-mail:m
8、celik@cs.umn.eduB.M.KazariswiththeOracleCorporation,Nashua,NH.E-mail
