Journal article // Inferensi






Penerapan Keluarga Model Spline Truncated Polinomial pada Regresi Nonparametrik
Andrea Tri Rian Dani, Ludia Ni’Matuzzahroh

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Abstract

One approach that is often used by researchers to determine the form of the relationship pattern between the response variables and predictor variables in regression analysis, namely the nonparametric approach, where the approach is used when the shape of the regression curve is assumed to be unknown. The truncated spline is a polynomial model in nonparametric regression that has segmented properties, where these properties provide better flexibility than ordinary polynomial models and are able to handle data whose behavior changes in certain sub-intervals due to the knot points in it. This study aims to apply a family of spline truncated polynomial models to nonparametric regression in the case of automotive data. The estimation method used is Ordinary Least Square (OLS). The number of knot points tested is 1 to 4-knot points with a degree of p=1,2,3. Based on the results of the analysis, the best model that produces the smallest GCV value is the nonparametric spline truncated quadratic regression model with 4 knots, which produces a GCV value of 522.27 and a coefficient of determination of 79.77%.

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