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EnglishThe present paper deals with nonlinear, non-monotonic data regression. This paper introduces an efficient algorithm to perform data transformation from non-monotonic to monotonic to be paired with a statistical bivariate regression method. The proposed algorithm is applied to a number of synthetic and real-world non-monotonic data sets to test its effectiveness. The proposed novel non-isotonic regression algorithm is also applied to a collection of data about strontium isotope stratigraphy and compared to a LOWESS regression tool.
| Titolo: | A novel non-isotonic statistical bivariate regression method-application to stratigraphic data modeling and interpolation | |
| Autori: | FIORI, Simone (Corresponding) | |
| Data di pubblicazione: | 2020 | |
| Rivista: | ||
| Abstract: | The present paper deals with nonlinear, non-monotonic data regression. This paper introduces an efficient algorithm to perform data transformation from non-monotonic to monotonic to be paired with a statistical bivariate regression method. The proposed algorithm is applied to a number of synthetic and real-world non-monotonic data sets to test its effectiveness. The proposed novel non-isotonic regression algorithm is also applied to a collection of data about strontium isotope stratigraphy and compared to a LOWESS regression tool. | |
| Handle: | http://hdl.handle.net/11566/286577 | |
| Appare nelle tipologie: | 1.1 Articolo in rivista |
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