## Abstract

The problem of estimation in binary response data has receivied a great number of alternative statistical solutions. Generalized linear models allow for a wide range of statistical models for regression data. The most used model is the logistic regression, see Hosmer et al. [6]. However, as Chen et al. [5] mentions, when the probability of a given binary response approaches 0 at a different rate than it approaches I, symmetric linkages are inappropriate. A class of models based on Weibull distribution indexed by three parameters is introduced here. Maximum likelihood methods are employed to estimate the parameters. The objective of the present paper is to show a solution for the estimation problem under the Weibull model. An example showing the quality of the model is illustrated by comparing it with the alternative probit and logit models.

Original language | English |
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Title of host publication | BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING |

Editors | PM Goggans, CY Chan |

Publisher | American Institute of Physics |

Pages | 187-193 |

Number of pages | 7 |

ISBN (Print) | 978-0-7354-0729-9 |

DOIs | |

Publication status | Published - 2009 |

Event | 29th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering - Oxford, United States Duration: 5 Jul 2009 → 10 Jul 2009 http://inspirehep.net/record/980193 |

### Publication series

Name | AIP Conference Proceedings |
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Publisher | AMER INST PHYSICS |

Volume | 1193 |

ISSN (Print) | 0094-243X |

### Conference

Conference | 29th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering |
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Abbreviated title | MaxEnt 2009 |

Country/Territory | United States |

City | Oxford |

Period | 5/07/09 → 10/07/09 |

Internet address |