MODELING PERCENTAGE OF POOR PEOPLE IN INDONESIA USING KERNEL AND FOURIER SERIES MIXED ESTIMATOR IN NONPARAMETRIC REGRESSION

MODELING PERCENTAGE OF POOR PEOPLE IN INDONESIA USING KERNEL AND FOURIER SERIES MIXED ESTIMATOR IN NONPARAMETRIC REGRESSION

dc.creatorBudiantara, I Nyoman
dc.creatorRatnasari, Vita
dc.creatorRatna, Madu
dc.creatorWibowo, Wahyu
dc.creatorAfifah, Ngizatul
dc.creatorPutri Rahmawati, Dyah
dc.creatorDwi Octavanny, Made Ayu
dc.date2023-04-11
dc.date.accessioned2023-05-22T20:48:53Z
dc.date.available2023-05-22T20:48:53Z
dc.identifierhttps://revistas.uh.cu/invoperacional/article/view/674
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/6330237
dc.descriptionPoverty is a very serious problem and often faced by the countries in the world, especially developing countries. The percentage of poor people in Indonesia reached 11.47 percent in 2013. The seven provinces with the highest poverty in Indonesia are Papua, West Papua, East Nusa Tenggara, Maluku, Gorontalo, Bengkulu and Aceh. This problem is modeled using mixed nonparametric regression of Kernel and Fourier Series. The response variable of this model is percentage of poor people (y), the predictor variables that follow Kernel regression curve are Mean of Years Schooling or MYS (v 1 ) and Literacy Rate or LR (v 2 ), whereas the predictor variable that follow the Fourier Series regression curve are Unemployment Rate or UR (t 1 ). This modeling produces R 2 = 62.78%.en-US
dc.descriptionPoverty is a very serious problem and often faced by the countries in the world, especially developing countries. The percentage of poor people in Indonesia reached 11.47 percent in 2013. The seven provinces with the highest poverty in Indonesia are Papua, West Papua, East Nusa Tenggara, Maluku, Gorontalo, Bengkulu and Aceh. This problem is modeled using mixed nonparametric regression of Kernel and Fourier Series. The response variable of this model is percentage of poor people (y), the predictor variables that follow Kernel regression curve are Mean of Years Schooling or MYS (v 1 ) and Literacy Rate or LR (v 2 ), whereas the predictor variable that follow the Fourier Series regression curve are Unemployment Rate or UR (t 1 ). This modeling produces R 2 = 62.78%.es-ES
dc.formatapplication/pdf
dc.languageeng
dc.publisherDepartamento de Matemática Aplicada. Facultad de Matemática y Computación. Universidad de La Habanaen-US
dc.relationhttps://revistas.uh.cu/invoperacional/article/view/674/589
dc.rightshttps://creativecommons.org/licenses/by/4.0es-ES
dc.sourceInvestigación Operacional; Vol. 40 No. 4 (2019): MODELLING IN REAL LIFE PROBLEMS CONTRIBUTIONSen-US
dc.sourceInvestigación Operacional; Vol. 40 Núm. 4 (2019): Special ISSUE CONTRIBUCIONES EN MODELACIÓN DE PROBLEMAS DE LA VIDA REALes-ES
dc.source2224-5405
dc.subjectFourier Serieses-ES
dc.subjectKerneles-ES
dc.subjectMixed Nonparametric Regressiones-ES
dc.subjectPercentage of Poor Peoplees-ES
dc.subjectFourier Seriesen-US
dc.subjectKernelen-US
dc.subjectMixed Nonparametric Regression,en-US
dc.subjectPercentage of Poor Peopleen-US
dc.titleMODELING PERCENTAGE OF POOR PEOPLE IN INDONESIA USING KERNEL AND FOURIER SERIES MIXED ESTIMATOR IN NONPARAMETRIC REGRESSIONen-US
dc.titleMODELING PERCENTAGE OF POOR PEOPLE IN INDONESIA USING KERNEL AND FOURIER SERIES MIXED ESTIMATOR IN NONPARAMETRIC REGRESSIONes-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typeArticlesen-US
dc.typeArtículoes-ES


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