Smoothing Spline Technique For Time Series Data with Autocorrelation - Adams, Samuel Olorunfemi
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The study proposes a smoothing method which is the arithmetic weighted value of Generalized Cross-Validation (GCV) and Unbiased Risk (UBR) methods. This study concluded that the PSM method provides the best-fit as a smoothing method, works well at autocorrelation levels ( =0.2, 0.5 and 0.8), and does not overfit time-series observations. The study recommended that the proposed smoothing is appropriate for time series observations with autocorrelation in the error term and econometrics real-life data. This study can be applied to: non - parametric regression, non - parametric forecasting, spatial, survival and econometrics observations.…mehr

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Produktbeschreibung
The study proposes a smoothing method which is the arithmetic weighted value of Generalized Cross-Validation (GCV) and Unbiased Risk (UBR) methods. This study concluded that the PSM method provides the best-fit as a smoothing method, works well at autocorrelation levels ( =0.2, 0.5 and 0.8), and does not overfit time-series observations. The study recommended that the proposed smoothing is appropriate for time series observations with autocorrelation in the error term and econometrics real-life data. This study can be applied to: non - parametric regression, non - parametric forecasting, spatial, survival and econometrics observations.
Autorenporträt
Dr. Samuel Olorunfemi Adams, Department of Statistics, Faculty of Science, University of Abuja, Abuja, Nigeria. Dr. Adams has well over 25 academic publications comprising of local and international journals as well as over 10 papers presented in learned conferences in and outside Nigeria.