Cargando…
Universal Linear Fit Identification: A Method Independent of Data, Outliers and Noise Distribution Model and Free of Missing or Removed Data Imputation
Data processing requires a robust linear fit identification method. In this paper, we introduce a non-parametric robust linear fit identification method for time series. The method uses an indicator 2/n to identify linear fit, where n is number of terms in a series. The ratio R (max) of a (max) − a...
Autores principales: | Adikaram, K. K. L. B., Hussein, M. A., Effenberger, M., Becker, T. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2015
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4646355/ https://www.ncbi.nlm.nih.gov/pubmed/26571035 http://dx.doi.org/10.1371/journal.pone.0141486 |
Ejemplares similares
-
Outlier Detection Method in Linear Regression Based on Sum of Arithmetic Progression
por: Adikaram, K. K. L. B., et al.
Publicado: (2014) -
Outlier Removal in Model-Based Missing Value Imputation for Medical Datasets
por: Huang, Min-Wei, et al.
Publicado: (2018) -
Time series outlier removal and imputing methods based on Colombian weather stations data
por: Parra-Plazas, Jaime, et al.
Publicado: (2023) -
Flexible imputation of missing data
por: van Buuren, Stef
Publicado: (2018) -
Missing Data and Imputation Methods
por: Schober, Patrick, et al.
Publicado: (2020)