### Abstract

A Poisson regression model is proposed for the analysis of incidence rates presented in a two-way table classified by two categorical variables. It is shown that the likelihood function is the same as that using Glasser's exponential covariate model. An algorithm is given to solve the maximum likelihood estimates of the regression parameters. The model is evaluated via deviance and the method is illustrated with an example. Some extensions of the model are discussed.

Original language | English (US) |
---|---|

Pages (from-to) | 215-218 |

Number of pages | 4 |

Journal | Methods of Information in Medicine |

Volume | 31 |

Issue number | 3 |

State | Published - Sep 1 1992 |

Externally published | Yes |

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### All Science Journal Classification (ASJC) codes

- Health Informatics
- Advanced and Specialized Nursing
- Health Information Management

### Cite this

*Methods of Information in Medicine*,

*31*(3), 215-218.

**Maximum likelihood estimation and testing of a poisson regression model.** / Wan, Jim; Galecki, A. T.

Research output: Contribution to journal › Article

*Methods of Information in Medicine*, vol. 31, no. 3, pp. 215-218.

}

TY - JOUR

T1 - Maximum likelihood estimation and testing of a poisson regression model.

AU - Wan, Jim

AU - Galecki, A. T.

PY - 1992/9/1

Y1 - 1992/9/1

N2 - A Poisson regression model is proposed for the analysis of incidence rates presented in a two-way table classified by two categorical variables. It is shown that the likelihood function is the same as that using Glasser's exponential covariate model. An algorithm is given to solve the maximum likelihood estimates of the regression parameters. The model is evaluated via deviance and the method is illustrated with an example. Some extensions of the model are discussed.

AB - A Poisson regression model is proposed for the analysis of incidence rates presented in a two-way table classified by two categorical variables. It is shown that the likelihood function is the same as that using Glasser's exponential covariate model. An algorithm is given to solve the maximum likelihood estimates of the regression parameters. The model is evaluated via deviance and the method is illustrated with an example. Some extensions of the model are discussed.

UR - http://www.scopus.com/inward/record.url?scp=0026915243&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0026915243&partnerID=8YFLogxK

M3 - Article

VL - 31

SP - 215

EP - 218

JO - Methods of Information in Medicine

JF - Methods of Information in Medicine

SN - 0026-1270

IS - 3

ER -