Bayesian modelling of catch in a north-west Atlantic fishery

被引:19
作者
Fernández, C
Ley, E
Steel, MFJ
机构
[1] Univ Kent, Inst Math & Stat, Canterbury CT2 7NF, Kent, England
[2] Univ St Andrews, St Andrews KY16 9AJ, Fife, Scotland
[3] Int Monetary Fund Inst, Washington, DC USA
关键词
Bayesian model averaging; categorical variables; Grand Bank fishery; predictive inference; probit model;
D O I
10.1111/1467-9876.00268
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
We model daily catches of fishing boats in the Grand Bank fishing grounds. We use data on catches per species for a number of vessels collected by the European Union in the context of the Northwest Atlantic Fisheries Organization. Many variables can be thought to influence the amount caught: a number of ship characteristics (such as the size of the ship, the fishing technique used and the mesh size of the nets) are obvious candidates, but one can also consider the season or the actual location of the catch. Our database leads to 28 possible regressors (arising from six continuous variables and fourcategorical variables, whose 22 levels are treated separately), resulting in a set of 177 million possible linear regression models for the log-catch. Zero observations are modelled separately through a probit model. Inference is based on Bayesian model averaging, using a Markov chain Monte Carlo approach. Particular attention is paid to the prediction of catches for single and aggregated ships.
引用
收藏
页码:257 / 280
页数:24
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