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12-25

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- Faculty Mathematics and Natural Science, Universitas Padjadjaran, Jalan Raya Bandung-Sumedang Km. 21 Jatinangor Sumedang 45363, Indonesia

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References

- [1] Zsuzsanna, T. & Marian, L. 2012. Multiple regression analysis of performance indicators in the ceramic industry. Procedia Economics and Finance, vol. 3, pp. 509-514.
- [2] Uyanik, G. K. & Guler, N. 2013. A study on multiple linear regression analysis. Procedia Social and Behavioral Sciences, vol. 106, pp. 234-240.
- [3] Desa, A., Yusoof, F., Ibrahim, N., Kadir, N. B. A. & Rahman, R. M. A. 2014. A study of the Relationship and Influence of Personality on Job Stress among Academic Administrators at a University. Procedia-Social and Behavioral Sciences, vol. 114, pp. 355-359.
- [4] Chen, S., Ding, C. H. Q. & Luo, B. 2018. Linear regression based projections for dimensionality reduction. Information Sciences, vol. 467, pp. 74-86.
- [5] Nurjannah, Rahardjo, S. S. & Sanusi, R. 2019. Linear Regression Analysis on the Determinants of Hypertension Prevention Behavior. Journal of Health Promotion and Behavior, vol. 4, no. 1, pp. 22-31.
- [6] Sun, X. & Sun, D. 2005. Estimation of the Cholesky decomposition of the covariance matrix for a conditional independent normal model. Statistics & Probability Letters, vol. 73, pp. 1-12.
- [7] Wang, J. & Liu, C. 2006. “Generating Multivariate Mixture of Normal Distributions Using a Modified Cholesky Decomposition. Proceedings of the Winter Simulation Conference, pp. 342-347.
- [8] Pourahmadi, M., Daniels, M. J. & Park, T. 2007. Simultaneous modelling of the Cholesky decomposition of several covariance matrices. Journal of Multivariate Analysis, vol. 98, pp. 568-587.
- [9] Alaba. O. O., Olubuseyo, O. E. & Oyebisi, O. 2013. Cholesky Decomposition of Variance-Covariance Matrix Effect on the Estimators of Seemingly Unrelated Regression Model. Journal of Science Research, vol. 12, pp. 371-380.
- [10] Chen, Z. & Leng, C. 2015. Local Linear Estimation of Covariance Matrices Via Cholesky Decomposition. Statistica Sinica, vol. 25, pp. 1249-1263.
- [11] Younis, G. 2015. “Practical Method to Solve Large Least Squares Problems Using Cholesky Decomposition.” Geodesy and Cartography, vol. 41, no. 3, pp. 113-118.
- [12] Babaie-Kafaki, S. & Roozbeh, M. 2017. A revised Cholesky decomposition to combat multicollinearity in multiple regression models. Journal of Statistical Computation and Simulation. Volume 87, Issue 12, Pages 2298-2308. doi:10.1080/00949655.2017.1328599
- [13] Huang, G., Liao, H. & Li, M. 2013. New formulation of Cholesky decomposition and applications in stochastic simulation. Probabilistic Engineering Mechanics, vol. 34, pp. 40-47.
- [14] Wang, M. & Ma, W. 2013. A structure-preserving algorithm for the quaternion Cholesky decomposition. Applied Mathematics and Computation, vol. 223, pp. 354-361.
- [15] He, D. & Xu, K. 2014. Estimation of the Cholesky decomposition in a conditional independent normal model with missing data. Statistics and Probability Letters, vol. 88, pp. 27–39.
- [16] Madar, V. 2015. Direct formulation to Cholesky decomposition of a general nonsingular correlation matrix. Statistics and Probability Letters, vol. 103, pp. 142-147.
- [17] Feng, S., Lian, H. & Xue, L. 2016. A new nested Cholesky decomposition and estimation for the covariance matrix of bivariate longitudinal data.” Computational Statistics and Data Analysis, vol. 102, pp. 98-109.
- [18] Lee, L., Baek, C. & Daniels, M. J. 2017. ARMA Cholesky factor models for the covariance matrix of linear models. Computational Statistics and Data Analysis, vol. 115, pp. 267-280.
- [19] Nino-Ruiz, E. D., Mancilla, A. & Calabria, J. C. 2017. A Posterior Ensemble Kalman Filter Based On A Modified Cholesky Decomposition. Procedia Computer Science, vol. 108C, pp. 2049-2058.
- [20] Okaze, T. & Mochida, A. 2017. Cholesky decomposition–based generation of artificial inflow turbulence including scalar fluctuation. Computers and Fluids, vol. 159, pp. 23-32.
- [21] Kokkinos, Y. & Margaritis, K. G. 2018. Managing the computational cost of model selection and cross-validation in extreme learning machines via Cholesky, SVD, QR and eigen decompositions. Neurocomputing, vol. 295, pp. 29-45.
- [22] Helmich-Paris, B., Repisky, M. & Visscher, L. 2019. Relativistic Cholesky-decomposed density matrix MP2. Chemical Physics, vol. 518, pp. 38-46.
- [23] Naf, J., Paolella, M. S. & Polak, P. 2019. Heterogeneous tail generalized COMFORT modeling via Cholesky decomposition. Journal of Multivariate Analysis, vol. 172, pp. 84-106.
- [24] Nino-Ruiz, E. D., Sandu, A. & Deng, X. 2019. A parallel implementation of the ensemble Kalman filter based on modified Cholesky decomposition. Journal of Computational Science, vol. 36, pp. 100654 (1-8).
- [25] Rawlings, J. O., Pantula, S. G. & Dickey, D. A. 1998. Applied Regression Analysis: A Research Tool, Second Edition. New York: Springer-Verlag.
- [26] Sarstedt, M. & Mooi, E. 2014. A Concise Guide to Market Research. Heidelberg: Springer-Verlag. doi: 10.1007/978-3-642-53965-7_7
- [27] Darlington, R. B. & Hayes, A. F. 2017. Regression Analysis and Linear Models: Concepts, Applications, and Implementation. New York: Guilford Press.
- [28] Thomas McSweeney, 2017. Modified Cholesky Decomposition and Applications. A dissertation submitted to the University of Manchester for the degree of Master of Science in the Faculty of Science and Engineering. Manchester Institute for Mathematical Sciences.
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- [30] Schmidt, A. F. & Finan, C. 2018. Linear regression and the normality assumption. Journal of Clinical Epidemiology, vol. 98, pp. 146-151.
- [31] Richard C. Aster, 2019. Parameter Estimation and Inverse Problems (Chapter Two: Linear Regression). Elsevier. doi:10.1016/B978-0-12-804651-7.00007-9

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bwmeta1.element.psjd-9b720a76-ef69-41bd-a395-a9c49e8fb49a