### Abstract

Original language | English |
---|---|

Title of host publication | KDD'16 / KDD 2016 |

Subtitle of host publication | Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 13-17, 2016, San Francisco, CA, USA |

Editors | Alex Smola, Charu Aggarwal |

Place of Publication | New York, NY, USA |

Publisher | Association for Computing Machinery (ACM) |

Pages | 1255-1264 |

Number of pages | 10 |

ISBN (Print) | 9781450342322 |

DOIs | |

Publication status | Published - 13 Aug 2016 |

Event | ACM International Conference on Knowledge Discovery and Data Mining 2016 - Hilton San Francisco Union Square, San Francisco, United States of America Duration: 13 Aug 2016 → 17 Aug 2016 Conference number: 22nd http://www.kdd.org/kdd2016/ |

### Conference

Conference | ACM International Conference on Knowledge Discovery and Data Mining 2016 |
---|---|

Abbreviated title | SIGKDD 2016 |

Country | United States of America |

City | San Francisco |

Period | 13/08/16 → 17/08/16 |

Other | KDD 2016, a premier interdisciplinary conference, brings together researchers and practitioners from data science, data mining, knowledge discovery, large-scale data analytics, and big data. |

Internet address |

### Keywords

- Hypothesis testing
- Multiple testing
- Model selection

### Cite this

*KDD'16 / KDD 2016: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 13-17, 2016, San Francisco, CA, USA*(pp. 1255-1264). New York, NY, USA: Association for Computing Machinery (ACM). https://doi.org/10.1145/2939672.2939775

}

*KDD'16 / KDD 2016: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 13-17, 2016, San Francisco, CA, USA.*Association for Computing Machinery (ACM), New York, NY, USA, pp. 1255-1264, ACM International Conference on Knowledge Discovery and Data Mining 2016, San Francisco, United States of America, 13/08/16. https://doi.org/10.1145/2939672.2939775

**A multiple test correction for streams and cascades of statistical hypothesis tests.** / Webb, Geoff I.; Petitjean, François.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

TY - GEN

T1 - A multiple test correction for streams and cascades of statistical hypothesis tests

AU - Webb, Geoff I.

AU - Petitjean, François

PY - 2016/8/13

Y1 - 2016/8/13

N2 - Statistical hypothesis testing is a popular and powerful tool for inferring knowledge from data. For every such test performed, there is always a non-zero probability of making a false discovery, i.e. rejecting a null hypothesis in error. Familywise error rate (FWER) is the probability of making at least one false discovery during an inference process. The expected FWER grows exponentially with the number of hypothesis tests that are performed, almost guaranteeing that an error will be committed if the number of tests is big enough and the risk is not managed; a problem known as the multiple testing problem. State-of-the-art methods for controlling FWER in multiple comparison settings require that the set of hypotheses be predetermined. This greatly hinders statistical testing for many modern applications of statistical inference, such as model selection, because neither the set of hypotheses that will be tested, nor even the number of hypotheses, can be known in advance. This paper introduces Subfamilywise Multiple Testing, a multiple-testing correction that can be used in applications for which there are repeated pools of null hypotheses from each of which a single null hypothesis is to be rejected and neither the specific hypotheses nor their number are known until the final rejection decision is completed. To demonstrate the importance and relevance of this work to current machine learning problems, we further refine the theory to the problem of model selection and show how to use Subfamilywise Multiple Testing for learning graphical models. We assess its ability to discover graphical models on more than 7,000 datasets, studying the ability of Subfamilywise Multiple Testing to outperform the state of the art on data with varying size and dimensionality, as well as with varying density and power of the present correlations. Subfamilywise Multiple Testing provides a significant improvement in statistical efficiency, often requiring only half as much data to discover the same model, while strictly controlling FWER.

AB - Statistical hypothesis testing is a popular and powerful tool for inferring knowledge from data. For every such test performed, there is always a non-zero probability of making a false discovery, i.e. rejecting a null hypothesis in error. Familywise error rate (FWER) is the probability of making at least one false discovery during an inference process. The expected FWER grows exponentially with the number of hypothesis tests that are performed, almost guaranteeing that an error will be committed if the number of tests is big enough and the risk is not managed; a problem known as the multiple testing problem. State-of-the-art methods for controlling FWER in multiple comparison settings require that the set of hypotheses be predetermined. This greatly hinders statistical testing for many modern applications of statistical inference, such as model selection, because neither the set of hypotheses that will be tested, nor even the number of hypotheses, can be known in advance. This paper introduces Subfamilywise Multiple Testing, a multiple-testing correction that can be used in applications for which there are repeated pools of null hypotheses from each of which a single null hypothesis is to be rejected and neither the specific hypotheses nor their number are known until the final rejection decision is completed. To demonstrate the importance and relevance of this work to current machine learning problems, we further refine the theory to the problem of model selection and show how to use Subfamilywise Multiple Testing for learning graphical models. We assess its ability to discover graphical models on more than 7,000 datasets, studying the ability of Subfamilywise Multiple Testing to outperform the state of the art on data with varying size and dimensionality, as well as with varying density and power of the present correlations. Subfamilywise Multiple Testing provides a significant improvement in statistical efficiency, often requiring only half as much data to discover the same model, while strictly controlling FWER.

KW - Hypothesis testing

KW - Multiple testing

KW - Model selection

UR - http://www.scopus.com/inward/record.url?scp=84984992257&partnerID=8YFLogxK

U2 - 10.1145/2939672.2939775

DO - 10.1145/2939672.2939775

M3 - Conference Paper

AN - SCOPUS:84984992257

SN - 9781450342322

SP - 1255

EP - 1264

BT - KDD'16 / KDD 2016

A2 - Smola, Alex

A2 - Aggarwal, Charu

PB - Association for Computing Machinery (ACM)

CY - New York, NY, USA

ER -