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All Journal Jurnal Matematika UNAND Inferensi
Putri Suci Aria
Department of Mathematics, Faculty of Information Technology, Batam Institute of Technology, Batam, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Mathematics Social Sciences

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PERFORMANCE COMPARISON OF SEMIPARAMETRIC KERNEL REGRESSION USING EPANECHNIKOV, GAUSSIAN, AND, BIWEIGHT KERNELS IN MODELING LIFE EXPECTANCY IN INDONESIA Cinta Rizki Oktarina; Sigit Nugroho; Idhia Sriliana; Putri Suci Aria
Jurnal Matematika UNAND Vol. 15 No. 3 (2026)
Publisher : Departemen Matematika dan Sains Data FMIPA Universitas Andalas Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jmua.15.3.406-417.2026

Abstract

Life expectancy is a crucial indicator of population health and socioeconomic development, and its relationship with explanatory variables often exhibits both linear and nonlinear patterns. This study employs a semiparametric kernel regression approach to model life expectancy in Indonesia by integrating parametric and nonparametric components. Undernutrition is modeled parametrically, while access to improved drinking water and the illiteracy rate are treated as nonparametric predictors to flexibly capture their nonlinear effects. The nonparametric component is estimated using the Nadaraya–Watson kernel estimator, whereas parameter estimation is conducted using the Ordinary Least Squares method. Several kernel functions are evaluated, and the optimal kernel and bandwidth are selected based on the minimum Generalized Cross-Validation (GCV) criterion. The results indicate that the Gaussian kernel with an optimal bandwidth of (100, 100) yields the best performance. The proposed model demonstrates excellent accuracy, with a coefficient of determination of 99.99% and a very low Root Mean Square Error (RMSE). These findings confirm that semiparametric kernel regression is a flexible and reliable method for modeling life expectancy and other public health indicators characterized by complex relationships.
Modeling Industrial and Maritime Sector Effects on Economic Growth in Riau Island Using GWNR-P Spline Cinta Rizki Oktarina; Andro Kurniawan; Sandy Salomo Saruan; Putri Suci Aria; Winalia Agwil
Inferensi Vol 9 No 2 (2026)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v9i2.10050

Abstract

Economic growth is an important indicator of regional development and is influenced by various socioeconomic factors. In the Riau Islands Province, the effects of the Labor Force Participation Rate (LFPR) and Capture Fishery Production Value on the Gross Regional Domestic Product (GRDP) percentage distribution may vary across districts and cities due to spatial heterogeneity and nonlinear relationships. Therefore, this study applies the GWNR-PSpline model to analyze these relationships. The optimal Penalized Spline model was obtained using knot points of 67.83 and 15.67 with a smoothing parameter of 50, as indicated by the minimum Generalized Cross Validation (GCV) value of 1245.917. Subsequently, the Manhattan distance and Bisquare kernel weighting scheme produced the minimum Cross Validation (CV) value of 64.51, indicating the lowest prediction error among the evaluated weighting combinations. The estimated local parameters varied across districts and cities, indicating spatial heterogeneity in the relationships between the explanatory variables and economic growth. Spatial analysis revealed that Natuna and Anambas Island exhibited stronger local effects than other regions. Furthermore, the local coefficients of determination ranged from 0.3944 to 0.8603, substantially exceeding the global P-Spline model (R2=0.158). These findings demonstrate that the GWNR-PSpline model effectively captures both nonlinear and spatially varying relationships.
Co-Authors Andro Kurniawan Cinta Rizki Oktarina Cinta Rizki Oktarina Idhia Sriliana Sandy Salomo Saruan Sigit Nugroho Winalia Agwil