Fast and covariate-adaptive method amplifies detection power in large-scale multiple hypothesis testing
Side information in addition to the p-values is often available in modern applications of multiple hypothesis testing. Here, the authors develop AdaFDR, a new statistical method for multiple hypothesis testing that adaptively learns the decision threshold and amplifies the discovery power.
Guardado en:
| Autores principales: | , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2019
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/857c63f9248a49b7a4152050747075ac |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Sumario: | Side information in addition to the p-values is often available in modern applications of multiple hypothesis testing. Here, the authors develop AdaFDR, a new statistical method for multiple hypothesis testing that adaptively learns the decision threshold and amplifies the discovery power. |
|---|