### Abstract

The zero-inflated negative binomial (ZINB) regression model with smoothing is introduced for modeling count data with many zero-valued observations, and its use is illustrated with shark bycatch data from the eastern Pacific Ocean tuna purse-seine fishery for 1994-2004. Based on the generalized information criterion, the ZINB regression model provided a better fit to the data than either Poisson, negative binomial or zero-inflated Poisson regression models. To demonstrate the utility of the ZINB regression model for the standardization of catch data, standardized temporal trends in bycatch rates estimated with the ZINB regression model are computed and compared to those obtained from fits of the other three types of models to the same data. With the exception of the negative binomial, estimated temporal trends were more similar among models than would have been inferred from an analysis of model fit. Comparison of trends among models suggests that the negative binomial regression model may overestimate model coefficients when fitted to data with many zero-valued observations.

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

Pages (from-to) | 210-221 |

Number of pages | 12 |

Journal | Fisheries Research |

Volume | 84 |

Issue number | 2 |

DOIs | |

Publication status | Published - 2007 Apr |

Externally published | Yes |

### Fingerprint

### Keywords

- CPUE
- EM algorithm
- GAM
- GLM
- Negative binomial
- Shark
- Spline smoothing
- Zero-inflated

### ASJC Scopus subject areas

- Aquatic Science

### Cite this

*Fisheries Research*,

*84*(2), 210-221. https://doi.org/10.1016/j.fishres.2006.10.019

**Modeling shark bycatch : The zero-inflated negative binomial regression model with smoothing.** / Minami, Mihoko; Lennert-Cody, C. E.; Gao, W.; Román-Verdesoto, M.

Research output: Contribution to journal › Article

*Fisheries Research*, vol. 84, no. 2, pp. 210-221. https://doi.org/10.1016/j.fishres.2006.10.019

}

TY - JOUR

T1 - Modeling shark bycatch

T2 - The zero-inflated negative binomial regression model with smoothing

AU - Minami, Mihoko

AU - Lennert-Cody, C. E.

AU - Gao, W.

AU - Román-Verdesoto, M.

PY - 2007/4

Y1 - 2007/4

N2 - The zero-inflated negative binomial (ZINB) regression model with smoothing is introduced for modeling count data with many zero-valued observations, and its use is illustrated with shark bycatch data from the eastern Pacific Ocean tuna purse-seine fishery for 1994-2004. Based on the generalized information criterion, the ZINB regression model provided a better fit to the data than either Poisson, negative binomial or zero-inflated Poisson regression models. To demonstrate the utility of the ZINB regression model for the standardization of catch data, standardized temporal trends in bycatch rates estimated with the ZINB regression model are computed and compared to those obtained from fits of the other three types of models to the same data. With the exception of the negative binomial, estimated temporal trends were more similar among models than would have been inferred from an analysis of model fit. Comparison of trends among models suggests that the negative binomial regression model may overestimate model coefficients when fitted to data with many zero-valued observations.

AB - The zero-inflated negative binomial (ZINB) regression model with smoothing is introduced for modeling count data with many zero-valued observations, and its use is illustrated with shark bycatch data from the eastern Pacific Ocean tuna purse-seine fishery for 1994-2004. Based on the generalized information criterion, the ZINB regression model provided a better fit to the data than either Poisson, negative binomial or zero-inflated Poisson regression models. To demonstrate the utility of the ZINB regression model for the standardization of catch data, standardized temporal trends in bycatch rates estimated with the ZINB regression model are computed and compared to those obtained from fits of the other three types of models to the same data. With the exception of the negative binomial, estimated temporal trends were more similar among models than would have been inferred from an analysis of model fit. Comparison of trends among models suggests that the negative binomial regression model may overestimate model coefficients when fitted to data with many zero-valued observations.

KW - CPUE

KW - EM algorithm

KW - GAM

KW - GLM

KW - Negative binomial

KW - Shark

KW - Spline smoothing

KW - Zero-inflated

UR - http://www.scopus.com/inward/record.url?scp=33847057703&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33847057703&partnerID=8YFLogxK

U2 - 10.1016/j.fishres.2006.10.019

DO - 10.1016/j.fishres.2006.10.019

M3 - Article

AN - SCOPUS:33847057703

VL - 84

SP - 210

EP - 221

JO - Fisheries Research

JF - Fisheries Research

SN - 0165-7836

IS - 2

ER -