A. Fanjul Hevia, W. González Manteiga, J. C. Pardo-Fernández

La curva ROC (del inglés, Receiver Operating Characteristic curve) es una herramienta estadística basada en las nociones de sensibilidad y especificidad que se utiliza para evaluar la capacidad discriminativa de un problema de clasificación. Cuando hay más de un método de clasificación se pueden utilizar estas curvas para comparar su comportamiento mediante la comparación de sus respectivas curvas ROC.
Por otra parte, hay ocasiones en las que la existencia de covariables afecta la capacidad de clasificación de estos métodos, por lo que hay que tenerlas en consideración. La curva ROC condicionada o la curva ROC ajustadas son dos formas de incluir esa información en el estudio.
En este trabajo se analiza, mediante contrastes no paramétricos, la relación de las curvas ROC condicionada, ajustada y la conjunta para comparar distintos métodos de clasificación y al mismo tiempo tener en cuenta la información de las covariables.

Keywords: Contrastes de hipótesis, covariables, curvas AROC, curvas ROC

Scheduled

GT18 Non-parametric statistics I. Non-parametric hypothesis tests
June 9, 2022  10:10 AM
A15


Other papers in the same session

Two-sample problem under random truncation

A. Lago Balseiro, J. de Uña Álvarez, J. C. Pardo-Fernández


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