المقارنة بين طريقتي انحدار اساس الشريحة و الشريحة التكعيبية في تقدير نموذج المعلمات المتغيرة زمنيا
Keywords:
نموذج المعلمات المتغيرة زمنيا ، الشريحة التكعيبية ، دوال الاساس ، معلمة التمهيد ، انحدار اساس الشريحةAbstract
This research involves the study of varying coefficient model and time-varying coefficient models, as they have attracted great interest in recent years. These methods were used as two methods, the Regression basis spline method and the cubic spline method. The simulation method is used for comparison. The nonparametric method shows the preference of the cubic spline method for penalized constraints by comparing three mathematical functions to represent variable parameters, different sample sizes, and different levels of model standard deviation.
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References
Ahkim, M., Gijbels, I., & Verhasselt, A. (2017). Shape testing in varying Coefficients models. Test, 26(2), 429-450.
Fan, J., & Zhang, W. (2008). Statistical methods with varying Coefficients models. Statistics and its Interface, 1(1), 179.
Gálvez, A., & Iglesias, A. (2013). Firefly algorithm for explicit B-spline curve fitting to data points. Mathematical Problems in Engineering, 2013..
Green, P. J. and Silverman, B. W. (1994). “Nonparametric Regression and Generalized Linear Models: a Roughness Penalty Approach”. Chapman and Hall, London .
Hastie, T., & Tibshirani, R. (1993). Varying‐Coefficients models. Journal of the Royal Statistical Society: Series B (Methodological), 55(4), 757-779.
Johnson, R.W., (2005). “A B-spline Collocation Method for solving the Incompressible Navier- Stokes Equations Using an ad hoc Method: the Boundary Residual Method,” Computers& Fluids 34: 121-149
Karagöz, R. (2020). Tensor Network B-splines for high-dimensional function approximation.
Kiebel, S., & Holmes, A. (2011). The general linear model. Statistical Parametric Mapping: The Analysis of Functional Brain Images, 101-125.
Rigollet, P., & Hütter, J. C. (2018). High dimensional statistics lecture notes. Accessed May, 2018.
Rodrıguez, G. (2001). Smoothing and non-parametric regression. Princeton University.
Wang, B. &Miao, Z.( 2014)." Comparative Analysis for Robust Penalized Spline Smoothing Method" Volume 2014, 11 pages
Wu, H., & Zhang, J. T. (2006). Nonparametric regression methods for longitudinal data analysis: mixed-effects modeling approaches. John Wiley & Sons.
Xue, L., & Qu, A. (2012). Variable selection in high-dimensional varying-Coefficients models with global optimality
