I-Regs (Internet-Regression Analysis) as a Statistical Innovation in Nonparametric Regression Modeling

Authors

  • Andrea Dani Universitas Mulawarman
  • I Nyoman Budiantara Institut Teknologi Sepuluh Nopember
  • Ulfa Siti Nuraini Universitas Negeri Surabaya
  • Wiyli Yustanti Universitas Negeri Surabaya
  • Sifriyani Universitas Mulawarman
  • Fachrian Bimantoro Putra Universitas Mulawarman

DOI:

https://doi.org/10.26740/jetis.v1i02.35288

Keywords:

I-Regs, Nonparametric Regression, Regression Analysis

Abstract

This research develops an information system based on the R-Shiny Dashboard, allowing users to perform nonparametric regression modeling. Internet-Regression Analysis (I-Regs) is the name of a dashboard that has been successfully developed. I-Regs provides a complete model library in regression analysis modeling, including parametric, nonparametric, and semiparametric regression. It is hoped that I-Regs can become a valuable tool for researchers, practitioners, and students in modeling regression analysis and solving various data analysis problems.

References

Afifah, N., Budiantara, I.N. and Latra, I.N. (2017) ‘Mixed Estimator of Kernel and Fourier Series in Semiparametric Regression’, in Journal of Physics: Conference Series. Institute of Physics Publishing. Available at: https://doi.org/10.1088/1742-6596/855/1/012002.

Biao, W.W. and Pourahmadi, M. (2003) Nonparametric estimation of large covariance matrices of longitudinal data, Biometrika. Available at: http://biomet.oxfordjournals.org/.

Bilodeau, M. (1992) ‘Fourier smoother and additive models’, Canadian Journal of Statistics, 20(3), pp. 257–269. Available at: https://doi.org/10.2307/3315313.

Budiantara, I.N. et al. (2019) ‘Modeling Percentage of Poor People In Indonesia Using Kernel and Fourier Series Mixed Estimator In Nonparametric Regression’, REVISTA INVESTIGACION OPERACIONAL, 40(4), pp. 538–550.

Du, P., Parmeter, C.F. and Racine, J.S. (2012) Nonparametric Kernel Regression with Multiple Predictors and Multiple Shape Constraints. Department of Economics; McMaster University; Canada.

Eubank, R.L. (1999) ‘Nonparametric Regression and Spline Smoothing’. New York: Marcel Dekker.

Guidoum, A.C. (2020) Kernel Estimator and Bandwidth Selection for Density and its Derivatives: The kedd Package. Algeria.

Hardle, W. (1994) ‘Applied Nonparametric Regression.’, in Humboldt-Universitat zu Berlin, Institut fur Statistik und Okonometrie. Berlin: Humboldt-Universitat zu Belrin. Available at: https://doi.org/10.2307/2348990.

Hidayat, R. et al. (2019) ‘Kernel-Spline Estimation of Additive Nonparametric Regression Model’, in IOP Conference Series: Materials Science and Engineering. Institute of Physics Publishing. Available at: https://doi.org/10.1088/1757-899X/546/5/052028.

Lin, D.Y. and Ying, Z. (2001) ‘Semiparametric and nonparametric regression analysis of longitudinal data’, Journal of the American Statistical Association, 96(453), pp. 103–113. Available at: https://doi.org/10.1198/016214501750333018.

Mariati, N.P.A.M., Budiantara, I.N. and Ratnasari, V. (2020) ‘Combination Estimation of Smoothing Spline and Fourier Series in Nonparametric Regression’, Journal of Mathematics, 2020. Available at: https://doi.org/10.1155/2020/4712531.

Mariati, N.P.A.M., Budiantara, I.N. and Ratnasari, V. (2021) ‘The application of mixed smoothing spline and fourier series model in nonparametric regression’, Symmetry, 13(11). Available at: https://doi.org/10.3390/sym13112094.

Ratnasari, V., Budiantara, I.N. and Dani, A.T.R. (2021) ‘Nonparametric Regression Mixed Estimators of Truncated Spline and Gaussian Kernel based on Cross-Validation (CV), Generalized Cross-Validation (GCV), and Unbiased Risk (UBR) Methods’, International Journal on Advanced Science, Engineering and Information Technology, 11(6).

Ratnasari, V., Utama, S.H. and Dani, A.T.R. (2024) ‘Toward Sustainable Development Goals (SDGs) with Statistical Modeling: Recursive Bivariate Binary Probit’, IAENG International Journal of Applied Mathematics, 54(8), pp. 1515–1521.

Regier, M.D. and Parker, R.D. (2015) ‘Smoothing using fractional polynomials: An alternative to polynomials and splines in applied research’, Wiley Interdisciplinary Reviews: Computational Statistics, 7(4), pp. 275–283. Available at: https://doi.org/10.1002/wics.1355.

Sifriyani et al. (2017) ‘Geographically weighted regression with spline approach’, Far East Journal of Mathematical Sciences, 101(6), pp. 1183–1196. Available at: https://doi.org/10.17654/MS101061183.

Sifriyani, S. et al. (2023) ‘Statistical Modeling: A New Regression Curve Approximation using Mixed Estimators Truncated Spline and Epanechnikov Kernel’, Engineering Letters, 31(4), pp. 1–7.

Suparti et al. (2019) ‘Modeling longitudinal data based on Fourier regression’, in Journal of Physics: Conference Series. Institute of Physics Publishing. Available at: https://doi.org/10.1088/1742-6596/1217/1/012105.

Suparti et al. (2021) ‘Biresponses kernel nonparametric regression: inflation and economic growth’, International Journal of Criminology and Sociology, 10, pp. 465–471. Available at: https://doi.org/10.6000/1929-4409.2021.10.54.

Wayan Sudiarsa, I. et al. (2015) ‘Combined estimator fourier series and spline truncated in multivariable nonparametric regression’, Applied Mathematical Sciences, 9(97–100), pp. 4997–5010. Available at: https://doi.org/10.12988/ams.2015.55394.

Widyastuti, D.A., Fernandes, A.A.R. and Pramoedyo, H. (2021) ‘Spline estimation method in nonparametric regression using truncated spline approach’, in Journal of Physics: Conference Series. IOP Publishing Ltd. Available at: https://doi.org/10.1088/1742-6596/1872/1/012027.

Wong, W.H. (1983) ‘On the Consistency of Cross-Validation in Kernel Nonparametric Regression’, The Annals of Statistics, 11(4), pp. 1136–1141.

Published

2025-06-25

How to Cite

Dani, A., Budiantara, I. N., Nuraini, U. S., Yustanti, W., Sifriyani, & Putra, F. B. (2025). I-Regs (Internet-Regression Analysis) as a Statistical Innovation in Nonparametric Regression Modeling. Journal of Education Technology and Information System, 1(02). https://doi.org/10.26740/jetis.v1i02.35288
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