{"product_id":"smoothing-spline-technique-for-time-series-data-with-autocorrelation-von-samuel-olorunfemi-adams","title":"Smoothing Spline Technique For Time Series Data with Autocorrelation","description":"\u003cp\u003eThe 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.\u003c\/p\u003e\u003cdiv class=\"aw-variant-hidden-subtitle-div\" id=\"aw-variant-subtitle-9786206151890\"\u003e\u003ch3\u003e\u003c\/h3\u003e\u003c\/div\u003e","brand":"Autorenwelt Shop","offers":[{"title":"Softcover - 9786206151890","offer_id":46665860645189,"sku":"9786206151890","price":68.9,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0940\/0622\/files\/ea178013-afeb-4029-a448-9aac4ffd9b42.jpg?v=1759295696","url":"https:\/\/shop.autorenwelt.de\/en\/products\/smoothing-spline-technique-for-time-series-data-with-autocorrelation-von-samuel-olorunfemi-adams","provider":"Autorenwelt Shop","version":"1.0","type":"link"}