Parameter estimation of S-distributions with alternating regression
We propose a novel 3-way alternating regression (3-AR) method as an effective strategy for the estimation of parameter values in S-distributions from frequency data. The 3-AR algorithm is very fast and performs well for error-free distributions and artificial noisy data obtained as random samples ge...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2007 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2099/3796 |
| Acceso en línea: | https://hdl.handle.net/2099/3796 |
| Access Level: | acceso abierto |
| Palabra clave: | Distribution (Probability theory) Inference Distribució (Teoria de la probabilitat) Inferència Classificació AMS::62 Statistics::62E Distribution theory Classificació AMS::62 Statistics::62G Nonparametric inference Classificació AMS::62 Statistics::62J Linear inference, regression |
| Sumario: | We propose a novel 3-way alternating regression (3-AR) method as an effective strategy for the estimation of parameter values in S-distributions from frequency data. The 3-AR algorithm is very fast and performs well for error-free distributions and artificial noisy data obtained as random samples generated from S-distributions, as well as for traditional statistical distributions and for actual observation data. In rare cases where the algorithm does not immediately converge, its enormous speed renders it feasible to select several initial guesses and search settings as an effective countermeasure. |
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