Repository Universitas Pakuan

Detail Karya Ilmiah Dosen

Nang Kurnia, Bagus Sartono, Muhammad Arna Ramadhan, Septian Rahardiantoro, Yusma Yanti

Judul : Stacking method for determining weights in partial least squares model averaging
Abstrak :

Model averaging has been developed to improve prediction accuracy in high dimensional regression. This approach is a weighted linear combination of some regression models. Two important procedures in model averaging are construction the candidate models and determination the weights. This paper evaluated performance of partial least squares model averaging (PLSMA) with some weights and proposed stacking as a method for determining the weights. Stacking determines the weights by minimizing least squares error over candidate models. Our proposed method (stacked-PLSMA) was evaluated in a simulation experiment and compared to equal weight, Akaike information criterion (AIC) weight, and Bayesian information criterion (BIC) weight. The result showed that stacked-PLSMA yielded smaller prediction error with high consistency than the other weights.

Tahun : 2021 Media Publikasi : Jurnal Internasional
Kategori : Jurnal No/Vol/Tahun : 2 / 11 / 2021
ISSN/ISBN : 1927-5307
PTN/S : Institut Pertanian Bogor Program Studi : ILMU KOMPUTER
Bibliography :

[1] M.R. Banan, K.D. Hjelmstad, Self-organization of architecture by simulated hierarchical adaptive random

partitioning, in: [Proceedings 1992] IJCNN International Joint Conference on Neural Networks, IEEE, Baltimore,

MD, USA, 1992: pp. 823–828.

[2] M.P. Perrone, Improving regression estimation: Averaging methods for variance reduction with extensions to

general convex measure optimization, PhD Thesis, Brown University, 1993.

[3] T. Ando, K.-C. Li, A Model-Averaging Approach for High-Dimensional Regression, J. Amer. Stat. Assoc. 109

(2014), 254–265.

[4] M.A. Ramadhan, B. Sartono, A. Kurnia, Partial least squares in constructing candidates model averaging. Int. J.

Sci. Res. Sci. Eng. Technol. 4(1) (2018), 1459-1463

[5] H. Akaike, Information Theory and an Extension of the Maximum Likelihood Principle, in: E. Parzen, K. Tanabe,

G. Kitagawa (Eds.), Selected Papers of Hirotugu Akaike, Springer New York, New York, NY, 1998: pp. 199–


[6] G. Claeskens, N.L. Hjort, Model selection and model averaging, Cambridge Books, Cambridge University Press, 2203



[7] A.E. Raftery, D. Madigan, J.A. Hoeting, Bayesian Model Averaging for Linear Regression Models, J. Amer.

Stat. Assoc. 92 (1997), 179–191.

[8] P.J. Brown, M. Vannucci, T. Fearn, Bayes model averaging with selection of regressors, J. R. Stat. Soc., Ser. B,

Stat. Methodol. 64 (2002), 519–536.

[9] C.L. Mallows, Some Comments on Cp, Technometrics. 42 (2000) 87–94.

[10] B.E. Hansen, Least Squares Model Averaging, Econometrica. 75 (2007), 1175–1189.

[11] Z. Yuan, Y. Yang, Combining Linear Regression Models: When and How? J. Amer. Stat. Assoc. 100 (2005),


[12] H.Wang, X. Zhang, G. Zou, Frequentist model averaging estimation: a review. J. Syst. Sci. Complex. 22(4)

(2009), 732–748.

[13] A. Ullah, H. Wang, Parametric and nonparametric frequentist model selection and model averaging.

Econometrics. 1 (2013, 157-179.

[14] E. Moral-Benito, Model averaging in economics: An overview. J. Econ. Surv. 29 (2015), 46-75.

[15] D.T. Salaki, A. Kurnia, B. Sartono, Model Averaging Method for Supersaturated Experimental Design, IOP

Conf. Ser.: Earth Environ. Sci. 31 (2016), 012016.

[16] S. Rahardiantoro, B. Sartono, A. Kurnia, Model Averaging for Predicting the Exposure to Aflatoxin B 1 Using

DNA Methylation in White Blood Cells of Infants, IOP Conf. Ser.: Earth Environ. Sci. 58 (2017), 012019.

[17] D. Posada, T.R. Buckley, Model selection and model averaging in phylogenetics: advantages of Akaike

information criterion and Bayesian approaches over likelihood ratio tests. Syst. Biol. 53(5) (2004), 793-808.

[18] S. Rahardiantoro, K.A. Notodiputro, A. Kurnia, Prediction Intervals of Model Averaging Methods for High

Dimensional Data. IOP Conf. Ser.: Earth Environ. Sci. 187(1) (2018), 012045

[19] D.T. Salaki, A. Kurnia, A. Gusnanto, I.W. Mangku, B. Sartono, Model averaging, an alternative approach to

model selection in high dimensional data estimation. Forum Stat. Komputasi: Indonesian J. Stat. 20(2) (2015),


[20] D.H. Wolpert, Stacked generalization. Neural Networks, 5(2) (1992), 241-259.

[21] L. Breiman, Stacked regressions. Mach. Learn. 24(1) (1996), 49-64.