MODEL REGRESI NONPARAMETRIK KERNEL MENGGUNAKAN ESTIMASI NADARAYA-WATSON UNTUK DATA HARGA INDEKS SAHAM GABUNGAN DI INDONESIA

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dc.contributor.authorKesumah, Dwika Irmayusmita Sandra
dc.contributor.supervisorEfendi, Rustam
dc.date.accessioned2022-09-15T08:43:42Z
dc.date.available2022-09-15T08:43:42Z
dc.date.issued2022-05
dc.description.abstractThe composite index is one of the stock price indexes in Indonesia. In this study the kernel nonparametric regression model with the Nadaraya-Watson estimator for the composite index data. The kernel regression method is one of the methods in nonparametric regression used to estimate conditional expectations using kernel functions. The kernel function used in this study is the Gaussian kernel function. The data used is the composite stock price index in Indonesia in 2018-2019. The first step is to determine in advance the optimal bandwidth with the Generalized Cross Validation (GCV) method. Kernel regression using gaussian kernel functions obtained a bandwidth value of 82,03 with an optimal GCV of 269,42. Based on the results of the analysis to measure the goodness of the model using Mean Absolute Square Error (MAPE) of 2,71, which means that the MAPE value is in the first category which is very good.en_US
dc.description.sponsorshipFakultas Matematika dan Ilmu Pengetahuan Alam Universitas Riauen_US
dc.identifier.citationPerpustakaanen_US
dc.identifier.otherElfitra
dc.identifier.urihttps://repository.unri.ac.id/handle/123456789/10670
dc.language.isoenen_US
dc.publisherElfitraen_US
dc.subjectNonparametric regressionen_US
dc.subjectNadaraya-Watson estimationen_US
dc.subjectGeneralized Cross Validationen_US
dc.subjectbandwidthen_US
dc.subjectMean Absolute Square Erroren_US
dc.titleMODEL REGRESI NONPARAMETRIK KERNEL MENGGUNAKAN ESTIMASI NADARAYA-WATSON UNTUK DATA HARGA INDEKS SAHAM GABUNGAN DI INDONESIAen_US
dc.typeArticleen_US

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