FN Archimer Export Format
PT J
TI A bootstrap method for estimating bias and variance in statistical fisheries modelling frameworks using highly disparate datasets
BT 
AF ELVARSSON, B. P.
   TAYLOR, L.
   TRENKEL, Verena
   KUPCA, V.
   STEFANSSON, G.
AS 1:1,2;2:1;3:3;4:4;5:1;
FF 1:;2:;3:PDG-RBE-EMH;4:;5:;
C1 Univ Iceland, Inst Sci, IS-107 Reykjavik, Iceland.
   Marine Res Inst, IS-121 Reykjavik, Iceland.
   IFREMER, Nantes, France.
   Umea Univ, High Performance Comp Ctr North HPC2N, Umea, Sweden.
C2 UNIV ICELAND, ICELAND
   MAR RES INST, ICELAND
   IFREMER, FRANCE
   UNIV UMEA, SWEDEN
SI NANTES
SE PDG-RBE-EMH
IN WOS Ifremer jusqu'en 2018
   copubli-europe
IF 1
TC 9
UR https://archimer.ifremer.fr/doc/00193/30459/29913.pdf
LA English
DT Article
DE ;bootstrapping;correlated data;fish population dynamics;non-linear models
AB Statistical models of marine ecosystems use a variety of data sources to estimate parameters using composite or weighted likelihood functions with associated weighting issues and questions on how to obtain variance estimates. Regardless of the method used to obtain point estimates, a method is required for variance estimation. A bootstrap technique is introduced for the evaluation of uncertainty in such models, taking into account inherent spatial and temporal correlations in the datasets, which are commonly transferred as assumptions from a likelihood estimation procedure into Hessian-based variance estimation procedures. The technique is demonstrated on a real dataset and the effects of the number of bootstrap samples on estimation bias and variance estimates are studied. Although the modelling framework and bootstrap method can be applied to multispecies and multiarea models, for clarity the case study described is of a single-species and single-area model.
PY 2014
SO African Journal Of Marine Science
SN 1814-232X
PU Natl Inquiry Services Centre Pty Ltd
VL 36
IS 1
UT 000336036600009
BP 99
EP 110
DI 10.2989/1814232X.2014.897253
ID 30459
ER

EF