Outliers detection and treatment: a review
Outliers are observations or measures that are suspicious because they are much smaller or much larger than the vast majority of the observations. These observations are problematic because they may not be caused by the mental process under scrutiny or may not reflect the ability under examination....
- Autores:
-
Cousineau, Denis
Chartier, Sylvain
- Tipo de recurso:
- Fecha de publicación:
- 2010
- Institución:
- Universidad de San Buenaventura
- Repositorio:
- Repositorio USB
- Idioma:
- spa
- OAI Identifier:
- oai:bibliotecadigital.usb.edu.co:10819/6496
- Acceso en línea:
- http://hdl.handle.net/10819/6496
- Palabra clave:
- Statistics
Outlier detection
Outlier treatment
Intervalos de confianza
Estadística de los intervalos
Guías
Representación gráfica
Encuestas nacionales
Aproximación Bayesiana
Estadística
Encuestas
- Rights
- License
- Atribución-NoComercial-SinDerivadas 2.5 Colombia
| Summary: | Outliers are observations or measures that are suspicious because they are much smaller or much larger than the vast majority of the observations. These observations are problematic because they may not be caused by the mental process under scrutiny or may not reflect the ability under examination. The problem is that a few outliers is sometimes enough to distort the group results (by altering the mean performance, by increasing variability, etc.). In this paper, various techniques aimed at detecting potential outliers are reviewed. These techniques are subdivided into two classes, the ones regarding univariate data and those addressing multivariate data. Within these two classes, we consider the cases where the population distribution is known to be normal, the population is not normal but known, or the population is unknown. Recommendations will be put forward in each case. |
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