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Anglický jazyk
Smoothing Spline Technique For Time Series Data with Autocorrelation
Autor: Samuel Olorunfemi Adams
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... Viac o knihe
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O knihe
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.
- Vydavateľstvo: LAP LAMBERT Academic Publishing
- Rok vydania: 2023
- Formát: Paperback
- Rozmer: 220 x 150 mm
- Jazyk: Anglický jazyk
- ISBN: 9786206151890
