REGRESI NONPARAMETRIK DENGAN MENGGUNAKAN METODE ROBUST CROSS-VALIDATION

• Main Authors: , RATNA YUNIARTI, , Prof. Dr. Sri Haryatmi, M.Sc.
• Format: Theses and Dissertations NonPeerReviewed
• Published: [Yogyakarta] : Universitas Gadjah Mada 2014
• Subjects: ETD
• Online Access: https://repository.ugm.ac.id/133714/; http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=74501
• Institution: Universitas Gadjah Mada

Regression analysis is a statistical tool that is widely used to determine the relationship between a pair of variables or more. If the formulation relationship between the predictor variablesX and Y the response variable is not known,estimation of the regression function m(:) can use a nonparametric approach. In nonprametric regression approach, generally just assumed regression function contained in a function space of infinite dimension. One approach, known in the nonparametric regression is the kernel regression. Nadaraya-Watson regression estimator is a kernel that can be used to estimating the regression function m(:). However, when the data are outliers estimators Nadaraya-Watson produces a large MSE. The influence of such outliers is causing large residuals of the model is formed, and the variance the data becomes larger. Therefore, we need a method to cope with outliers. One method that can overcome the outliers is a robust method. Huber introduced estimator-M, the idea that a robust estimator against outliers. In addition, also required a method to estimate the error prediction error a model, it is cross-validation method. Cross validation is a methods that can be used to obtain the best regression curve models. Cross-validation can estimate the prediction error of a model and also compare existing models and then selected models which has a lower prediction error.