Abstract
We investigated the finite properties as well as the goodness of fit test for the cubic smoothing spline selection methods like the Generalized Maximum Likelihood (GML), Generalized Cross-Validation (GCV) and Mallow CP criterion (MCP) estimators for time-series observation when there is the presence of Autocorrelation in the error term of the model. The Monte-Carlo study considered 1,000 replication with six sample sizes: 30; 60; 120; 240; 480 and 960, four degree of autocorrelations; 0.1; 0.3; 0.5; and 0.9 and three smoothing parameters; lambdaGML= 0.07271685, lambdaGCV= 0.005146929, lambdaMCP= 0.7095105. The cubic smoothing spline selection methods were also applied to a real-life dataset. The Predictive mean square error, R-square, and adjusted R-square criteria for assessing finite properties and goodness of fit among competing models discovered that the performance of the estimators is affected by changes in the sample sizes and autocorrelation levels of the simulated and real-life data set. The study concluded that the Generalized Cross-Validation estimator provides a better fit for Autocorrelated time series observation. It is recommended that the GCV works well at the four autocorrelation levels and provides the best fit for time-series observations at all sample sizes considered. This study can be applied to; non –parametric regression, non –parametric forecasting, spatial, survival and econometric observations.
Publisher
Nigerian Society of Physical Sciences
Subject
General Physics and Astronomy,General Mathematics,General Chemistry
Reference27 articles.
1. Q.Kong, T. Siauw &A.M.Bayen, Python Programming and Numerical Methods: A Guide for Engineers and Scientist, Elsevier, ISBN: 978-0-12819549-9. (2020) https://doi.org/10.1016/C2018-0-04165-1
2. R. G. McClarren, Computational nuclear engineering and radiological science using python, https://doi.org/10.1016/C2016-0-03507-16 Elsevier (2018) 439. [3] J. R. Buchanan, “Cubic Spline Interpolation: MATH 375, Numerical Analysis”, Banach. Millersville.edu (2010).
3. J. Chen, “Testing goodness of fit of polynomial models via spline smoothing techniques”, Statistics and Probability Letters 19 (1994) 65. https://doi.org/10.1016/0167-7152(94)90070-1
4. N. Caouder & S. Huet, “Testing goodness-of-fit for nonlinear regression models with heterogeneous variances”, Computational Statistics and Data Analysis 23 (1998) 491. https://doi.org/10.1016/S0167-9473(96)00049-1
5. C.M. Crainiceanua & D. Ruppert, “Likelihood ratio tests for goodnessof-fit of a nonlinear regression model”, Journal of Multivariate Analysis 91 (2004) 35.
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