Comparative Study of GCV-MCP Hybrid Smoothing Methods for Predicting Time Series Observations
American Journal of Theoretical and Applied Statistics
Volume 9, Issue 5, September 2020, Pages: 219-227
Received: Jun. 15, 2020; Accepted: Jul. 7, 2020; Published: Oct. 12, 2020
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Authors
Samuel Olorunfemi Adams, Department of Statistics, University of Abuja, Abuja, Nigeria
Yahaya Haruna Umar, Department of Statistics, University of Abuja, Abuja, Nigeria
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Abstract
Generalized Cross Validation (GCV) has been considered a popular model for choosing the complexity of statistical models, it is also well known for its optimal properties. Mallow’s CP criterion (MCP) has been considered a powerful tool which is used to select smoothing parameters for spline estimates with non-Gaussian data. Most of the past works applied Generalized Cross Validation (GCV) and Mallow’s CP criterion (MCP) smoothing methods to time series data, this methods over fits data in the presence of Autocorrelation error. A new Smoothing method is proposed by taking the hybrid of Generalized Cross Validation (GCV) and Mallow’s CP criterion (MCP). The predicting performance of the Hybrid GCV-MCP is compared with Generalized Cross Validation (GCV) and Mallow’s CP criterion (MCP) using data generated through a simulation study and real-life data on all SITC export and import price index in Nigeria between the years, 2001-2018, performed by using a program written in R and based on the predictive Mean Score Error (PMSE) criterion. Experimental results obtained show that the predictive mean square error (PMSE) of the three smoothing methods decreases as the sample size and smoothing parameters increases. The study discovered that the Hybrid GCV-MCP smoothing methods performed better than the classical GVV and MCP for both the simulated and real life data.
Keywords
Time Series Predicting, Generalized Cross Validation, Mallow’s CP Criterion, Hybrid GCV-MCP
To cite this article
Samuel Olorunfemi Adams, Yahaya Haruna Umar, Comparative Study of GCV-MCP Hybrid Smoothing Methods for Predicting Time Series Observations, American Journal of Theoretical and Applied Statistics. Vol. 9, No. 5, 2020, pp. 219-227. doi: 10.11648/j.ajtas.20200905.15
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Copyright © 2020 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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