## Abstract

The usual way of conducting empirical comparisons among competing polynomial model selection criteria is by generating artificial data from created true models with specified link weights. The robustness of each model selection criterion is then judged by its ability to recover the true model from its sample data sets with varying sizes and degrees of noise.

If we have a set of multivariate real data and have empirically found a polynomial regression model that is so far seen as the right model represented by the data, we would like to be able to replicate the multivariate data artificially to enable us to run multiple experiments to achieve two objectives. First, to see if the model selection criteria can recover the model that is seen to be the right model. Second, to find out the minimum sample size required to recover the right model.

This paper proposes a methodology to replicate real multivariate data using its covariance matrix and a polynomial regression model seen as the right model represented by the data. The sample data sets generated are then used for model discovery experiments.

If we have a set of multivariate real data and have empirically found a polynomial regression model that is so far seen as the right model represented by the data, we would like to be able to replicate the multivariate data artificially to enable us to run multiple experiments to achieve two objectives. First, to see if the model selection criteria can recover the model that is seen to be the right model. Second, to find out the minimum sample size required to recover the right model.

This paper proposes a methodology to replicate real multivariate data using its covariance matrix and a polynomial regression model seen as the right model represented by the data. The sample data sets generated are then used for model discovery experiments.

Original language | English |
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Title of host publication | Advances in Intelligent Data Analysis |

Subtitle of host publication | 4th International Conference, IDA 2001 Cascais, Portugal, September 13-15, 2001 Proceedings |

Editors | Frank Hoffmann, David J. Hand, Niall Adams, Douglas Fisher, Gabriela Guimaraes |

Place of Publication | Berlin Germany |

Publisher | Springer |

Pages | 370-377 |

Number of pages | 8 |

ISBN (Print) | 3540425810 |

DOIs | |

Publication status | Published - 2001 |

Event | 4th International Conference on Intelligent Data Analysis, IDA 2001 - Cascais, Portugal Duration: 13 Sept 2001 → 15 Sept 2001 |

### Publication series

Name | Lecture Notes in Computer Science |
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Publisher | Springer |

Volume | 2189 |

ISSN (Print) | 0302-9743 |

### Conference

Conference | 4th International Conference on Intelligent Data Analysis, IDA 2001 |
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Country/Territory | Portugal |

City | Cascais |

Period | 13/09/01 → 15/09/01 |