### Abstract

We study the problem of maximum entropy density estimation in the presence of known sample selection bias. We propose three bias correction approaches. The first one takes advantage of unbiased sufficient statistics which can be obtained from biased samples. The second one estimates the biased distribution and then factors the bias out. The third one approximates the second by only using samples from the sampling distribution. We provide guarantees for the first two approaches and evaluate the performance of all three approaches in synthetic experiments and on real data from species habitat modeling, where maxent has been successfully applied and where sample selection bias is a significant problem.

Original language | English (US) |
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Title of host publication | Advances in Neural Information Processing Systems 18 - Proceedings of the 2005 Conference |

Pages | 323-330 |

Number of pages | 8 |

State | Published - Dec 1 2005 |

Event | 2005 Annual Conference on Neural Information Processing Systems, NIPS 2005 - Vancouver, BC, Canada Duration: Dec 5 2005 → Dec 8 2005 |

### Publication series

Name | Advances in Neural Information Processing Systems |
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ISSN (Print) | 1049-5258 |

### Other

Other | 2005 Annual Conference on Neural Information Processing Systems, NIPS 2005 |
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Country | Canada |

City | Vancouver, BC |

Period | 12/5/05 → 12/8/05 |

### All Science Journal Classification (ASJC) codes

- Computer Networks and Communications
- Information Systems
- Signal Processing

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## Cite this

*Advances in Neural Information Processing Systems 18 - Proceedings of the 2005 Conference*(pp. 323-330). (Advances in Neural Information Processing Systems).